Calibration of glucose monitoring sensor and/or insulin delivery system

ABSTRACT

Disclosed are methods, apparatuses, etc. for calibrating glucose monitoring sensors and/or insulin delivery systems. In certain example embodiments, blood glucose reference samples may be correlated with sensor measurements with regard to a delay associated with the sensor measurements. In certain other example embodiments, one or more parameters of a probability model may be estimated based on blood glucose reference sample-sensor measurement pairs. Based on such information, function(s) for estimating a blood-glucose concentration in a patient may be determined.

BACKGROUND

1. Field

Subject matter disclosed herein relates to calibrating a glucosemonitoring sensor and/or an insulin delivery system including, by way ofexample but not limitation, calibration that is at least partiallyautomatic and/or a calibration of sensor current measurements duringoperation.

2. Information

The pancreas of a normal healthy person produces and releases insulininto the blood stream in response to elevated blood plasma glucoselevels. Beta cells (β-cells), which reside in the pancreas, produce andsecrete insulin into the blood stream as it is needed. If β-cells becomeincapacitated or die, a condition known as Type I diabetes mellitus (orin some cases, if β-cells produce insufficient quantities of insulin, acondition known as Type II diabetes), then insulin may be provided to abody from another source to maintain life or health.

Traditionally, because insulin cannot be taken orally, insulin has beeninjected with a syringe. More recently, the use of infusion pump therapyhas been increasing in a number of medical situations, including fordelivering insulin to diabetics. For example, external infusion pumpsmay be worn on a belt, in a pocket, or the like, and they can deliverinsulin into a body via an infusion tube with a percutaneous needle or acannula placed in subcutaneous tissue.

As of 1995, less than 5% of Type I diabetics in the United States wereusing infusion pump therapy. Presently, over 7% of the more than 900,000Type I diabetics in the U.S. are using infusion pump therapy. Thepercentage of Type I diabetics that use an infusion pump is growing at arate of over 2% each year. Moreover, the number of Type II diabetics isgrowing at 3% or more per year, and growing numbers of insulin-usingType II diabetics are also adopting infusion pumps. Additionally,physicians have recognized that continuous infusion can provide greatercontrol of a diabetic's condition, so they too are increasinglyprescribing it for patients.

A closed-loop infusion pump system may include an infusion pump that isautomatically and/or semi-automatically controlled to infuse insulininto a patient. The infusion of insulin may be controlled to occur attimes and in amounts that are based, for example, upon blood glucosemeasurements obtained from an embedded blood-glucose sensor in, e.g.,real-time. Closed-loop infusion pump systems may also employ thedelivery of glucagon, in addition to the delivery of insulin, forcontrolling blood-glucose and/or insulin levels of a patient (e.g., in ahypoglycemic context).

SUMMARY

Briefly, example embodiments may relate to methods, systems,apparatuses, and/or articles, etc. for calibrating glucose monitoringsensors and/or insulin delivery systems. Glucose monitoring sensorsand/or insulin delivery systems, including those that are designed tooperate continually (e.g., repeatedly, at regular intervals, at leastsubstantially continuously, etc.), may be calibrated. More specifically,but by way of example only, such calibration may be at least automaticor semi-automatic and/or calibration of sensor current measurements maybe performed online (e.g., during operation of an associated system).

In one or more example embodiments, a method may include: correlatingblood glucose reference samples with sensor measurements to provide atleast one output signal responsive to a delay associated with the sensormeasurements; and determining a function for estimating a blood-glucoseconcentration in a patient from sensor measurements based, at least inpart, on the at least one output signal.

In at least one example implementation, the correlating may furtherinclude: applying the blood glucose reference samples and the sensormeasurements to a matched filter at multiple time shift delays toascertain the delay. In at least one other example implementation, thecorrelating may further include: correlating the sensor measurementswith the blood glucose reference samples at multiple different timedelays to ascertain the delay. In at least one other exampleimplementation, the determining may further include: applying the bloodglucose reference samples and the sensor measurements to a Wiener filterin conjunction with the delay to determine multiple filter coefficients.

In at least one other example implementation, the determining mayfurther include: determining a function for estimating a blood-glucoseconcentration in the patient from sensor measurements based, at least inpart, on a noise signal that is associated with the sensor measurements.In at least one other example implementation, the sensor measurementsmay comprise current sensor measurements taken from interstitial fluidof the patient. In at least one other example implementation, thedetermining may further include: determining a function for estimating ablood-glucose concentration in the patient, the function to account forthe delay; the delay representing, at least partially, an approximateddelay associated with blood glucose diffusion between one or more bloodvessels and interstitial fluid of the patient.

In at least one other example implementation, the determining mayfurther include: determining a slope and an offset for the function forestimating a blood-glucose concentration in a patient. In yet at leastone other example implementation, the determining may further include:determining the slope and the offset for the function using a Bayesiantechnique in which a parameter vector includes a calfactor variable andan offset variable and in which an independent variable includes acurrent signal corresponding to the sensor measurements. In yet at leastone other example implementation, the determining may further include:determining the slope and the offset for the function using a linearKalman filter technique in which a parameter vector includes a calfactorvariable and an offset variable.

In at least one other example implementation, the method may furtherinclude: taking the sensor measurements that are to be correlated usingone or more subcutaneous current sensors; and infusing insulin into thepatient based on the function for estimating a blood-glucoseconcentration in the patient.

In one or more example embodiments, an apparatus may include a filterunit to receive one or more signals based on blood-glucose sensormeasurements, the filter unit may include one or more processors to:correlate blood glucose reference samples with sensor measurements toprovide at least one output signal responsive to a delay associated withthe sensor measurements; and determine a function for estimating ablood-glucose concentration in a patient from sensor measurements based,at least in part, on the at least one output signal.

In at least one example implementation, the filter unit may be capableof correlating the blood glucose reference samples with the sensormeasurements by: applying the blood glucose reference samples and thesensor measurements to a matched filter at multiple time shift delays toascertain the delay. In at least one other example implementation, thefilter unit may be capable of correlating the blood glucose referencesamples with the sensor measurements by: correlating the sensormeasurements with the blood glucose reference samples at multipledifferent time delays to ascertain the delay. In at least one otherexample implementation, the filter unit may be capable of determiningthe function for estimating the blood-glucose concentration in thepatient by: applying the blood glucose reference samples and the sensormeasurements to a Wiener filter in conjunction with the delay todetermine multiple filter coefficients.

In at least one other example implementation, the filter unit may becapable of determining the function for estimating the blood-glucoseconcentration in the patient by: determining a function for estimating ablood-glucose concentration in the patient from sensor measurementsbased, at least in part, on a noise signal that is associated with thesensor measurements. In at least one other example implementation, thesensor measurements may comprise current sensor measurements taken frominterstitial fluid of the patient. In at least one other exampleimplementation, the filter unit may be capable of determining thefunction for estimating the blood-glucose concentration in the patientby: determining a function for estimating a blood-glucose concentrationin the patient, the function to account for the delay; the delayrepresenting, at least partially, an approximated delay associated withblood glucose diffusion between blood vessels and interstitial fluid ofthe patient.

In at least one other example implementation, the filter unit may becapable of determining the function for estimating the blood-glucoseconcentration in the patient by: determining a slope and an offset forthe function for estimating a blood-glucose concentration in a patient.In yet at least one other example implementation, the filter unit may becapable of determining the function for estimating the blood-glucoseconcentration in the patient by: determining the slope and the offsetfor the function using a Bayesian technique in which a parameter vectorincludes a calfactor variable and an offset variable and in which anindependent variable includes current signal corresponding to the sensormeasurements. In yet at least one other example implementation, thefilter unit may be capable of determining the function for estimatingthe blood-glucose concentration in the patient by: determining the slopeand the offset for the function using a linear Kalman filter techniquein which a parameter vector includes a calfactor variable and an offsetvariable.

In at least one other example implementation, the apparatus may furtherinclude: one or more blood-glucose subcutaneous current sensors adaptedto be coupled to the patient to obtain blood-glucose sensor measurementsand adapted to provide the one or more signals based on theblood-glucose sensor measurements; with the filter unit being capable ofobtaining the blood-glucose sensor measurements via the one or moreblood-glucose subcutaneous current sensors using the one or moresignals; and at least one insulin delivery system adapted to infuseinsulin into the patient based on the function for estimating ablood-glucose concentration in the patient.

In one or more example embodiments, a system may include: means forcorrelating blood glucose reference samples with sensor measurements toprovide at least one output signal responsive to a delay associated withthe sensor measurements; and means for determining a function forestimating a blood-glucose concentration in a patient from sensormeasurements based, at least in part, on the at least one output signal.

In one or more example embodiments, an article may include at least onestorage medium having stored thereon instructions executable by one ormore processors to: correlate blood glucose reference samples withsensor measurements to provide at least one output signal responsive toa delay associated with the sensor measurements; and determine afunction for estimating a blood-glucose concentration in a patient fromsensor measurements based, at least in part, on the at least one outputsignal.

In one or more example embodiments, a method may include: obtainingmultiple blood glucose reference sample-sensor measurement pairs;estimating one or more parameters of a probability model based, at leastin part, on the blood glucose reference sample-sensor measurement pairs;and determining a function for estimating a blood-glucose concentrationin a patient from sensor measurements based, at least in part, on theestimated one or more parameters.

In at least one example implementation, the obtaining may furtherinclude: taking sensor measurements for the multiple blood glucosereference sample-sensor measurement pairs using at least onesubcutaneous current sensor. In at least one other exampleimplementation, the function may be defined at least partly by a slopeand an offset. In at least one other example implementation, the one ormore parameters may comprise a calfactor variable and an offsetvariable. In yet at least one other example implementation, arelationship between current sensor measurements corresponding to theblood glucose reference sample-sensor measurement pairs and sensorglucose concentration for the patient may be represented by a linearmodel, the linear model may be associated with a slope and an offset;and the slope and the offset of the linear model may be determinablefrom the calfactor variable and/or the offset variable.

In at least one other example implementation, the estimating may furtherinclude: estimating the one or more parameters using a linear Kalmanfilter estimator in which process noise and measurement noise aremodeled as being constant. In at least one other example implementation,the estimating may further include: estimating the one or moreparameters using a linear Kalman filter estimator in which process noiseis adapted as a function of model performance. In at least one otherexample implementation, the estimating may further include: estimatingthe one or more parameters using a linear Kalman filter estimator inwhich measurement noise is adapted as a function of model performance.In at least one other example implementation, the estimating may furtherinclude: estimating the one or more parameters using a Bayesianestimator in which sensor measurements corresponding to the bloodglucose reference sample-sensor measurement pairs may comprise anindependent variable and blood glucose reference samples correspondingto the blood glucose reference sample-sensor measurement pairs maycomprise a measured variable.

In at least one other example implementation, the method may furtherinclude: estimating a composite sensor glucose concentration value forthe patient using multiple functions and at least one weighting factorthat is derived from one or more quality indicators for multipleprobability models.

In at least one other example implementation, the determining mayfurther include determining multiple functions for estimating ablood-glucose concentration in the patient from sensor measurements; andthe method may further include: estimating multiple sensor glucoseconcentration values for the patient using the multiple functions; themultiple functions associated with multiple probability models, whichinclude the probability model; and determining a composite sensorglucose concentration value for the patient based, at least partly, onthe multiple sensor glucose concentration values. In yet at least oneother example implementation, the determining a composite sensor glucoseconcentration value for the patient may further include: weighting themultiple sensor glucose concentration values based, at least in part, onmultiple quality indicators indicative of an accuracy of the multipleprobability models. In yet at least one other example implementation,the multiple quality indicators may comprise multiple likelihood values;and the method may further include: calculating the multiple likelihoodvalues based, at least partly, on error differences between sensorglucose concentration values estimated from the multiple probabilitymodels and reference blood glucose values from blood glucose referencesamples.

In one or more example embodiments, an apparatus may include acalibration unit to receive one or more signals based on blood-glucosesensor measurements, the calibration unit may include one or moreprocessors to: obtain multiple blood glucose reference sample-sensormeasurement pairs; estimate one or more parameters of a probabilitymodel based, at least in part, on the blood glucose referencesample-sensor measurement pairs; and determine a function for estimatinga blood-glucose concentration in a patient from sensor measurementsbased, at least in part, on the estimated one or more parameters.

In at least one example implementation, the calibration unit may becapable of obtaining the multiple blood glucose reference sample-sensormeasurement pairs by: taking sensor measurements for the multiple bloodglucose reference sample-sensor measurement pairs using at least onesubcutaneous current sensor. In at least one other exampleimplementation, the function may be defined at least partly by a slopeand an offset.

In at least one other example implementation, the one or more parametersmay comprise a calfactor variable and an offset variable. In yet atleast one other example implementation, a relationship between currentsensor measurements corresponding to the blood glucose referencesample-sensor measurement pairs and sensor glucose concentration for thepatient may be represented by a linear model, and the linear model maybe associated with a slope and an offset; and the slope and the offsetof the linear model may be determinable from the calfactor variableand/or the offset variable.

In at least one other example implementation, the calibration unit maybe capable of estimating the one or more parameters of a probabilitymodel by: estimating the one or more parameters using a linear Kalmanfilter estimator in which process noise and measurement noise aremodeled as being constant. In at least one other example implementation,the calibration unit may be capable of estimating the one or moreparameters of a probability model by: estimating the one or moreparameters using a linear Kalman filter estimator in which process noiseis adapted as a function of model performance. In at least one otherexample implementation, the calibration unit may be capable ofestimating the one or more parameters of a probability model by:estimating the one or more parameters using a linear Kalman filterestimator in which measurement noise is adapted as a function of modelperformance.

In at least one other example implementation, the calibration unit maybe capable of estimating the one or more parameters of a probabilitymodel by: estimating the one or more parameters using a Bayesianestimator in which sensor measurements corresponding to the bloodglucose reference sample-sensor measurement pairs may comprise anindependent variable and blood glucose reference samples correspondingto the blood glucose reference sample-sensor measurement pairs maycomprise a measured variable. In at least one other exampleimplementation, the one or more processors of the calibration unit mayfurther be to: estimate a composite sensor glucose concentration valuefor the patient using multiple functions and at least one weightingfactor that is derived from one or more quality indicators for multipleprobability models.

In at least one other example implementation, the calibration unit maybe capable of determining the function for estimating a blood-glucoseconcentration in the patient by determining multiple functions forestimating a blood-glucose concentration in the patient from sensormeasurements; and the one or more processors of the calibration unit mayfurther be to: estimate multiple sensor glucose concentration values forthe patient using the multiple functions; the multiple functionsassociated with multiple probability models, which include theprobability model; and determine a composite sensor glucoseconcentration value for the patient based, at least partly, on themultiple sensor glucose concentration values. In at least one otherexample implementation, the calibration unit may be capable ofdetermining the composite sensor glucose concentration value for thepatient by: weighting the multiple sensor glucose concentration valuesbased, at least in part, on multiple quality indicators indicative of anaccuracy of the multiple probability models. In at least one otherexample implementation, the multiple quality indicators may comprisemultiple likelihood values; and the one or more processors of thecalibration unit may further be to: calculate the multiple likelihoodvalues based, at least partly, on error differences between sensorglucose concentration values estimated from the multiple probabilitymodels and reference blood glucose values from blood glucose referencesamples.

In at least one other example implementation, the apparatus may furtherinclude: one or more blood-glucose sensors adapted to be coupled to thepatient to obtain blood-glucose sensor measurements and adapted toprovide the one or more signals based on the blood-glucose sensormeasurements, the calibration unit may be capable of obtaining themultiple blood glucose reference sample-sensor measurement pairs via theone or more blood-glucose sensors using the one or more signals.

In one or more example embodiments, a system may include: means forobtaining multiple blood glucose reference sample-sensor measurementpairs; means for estimating one or more parameters of a probabilitymodel based, at least in part, on the blood glucose referencesample-sensor measurement pairs; and means for determining a functionfor estimating a blood-glucose concentration in a patient from sensormeasurements based, at least in part, on the estimated one or moreparameters.

In one or more example embodiments, an article may include at least onestorage medium having stored thereon instructions executable by one ormore processors to: obtain multiple blood glucose referencesample-sensor measurement pairs; estimate one or more parameters of aprobability model based, at least in part, on the blood glucosereference sample-sensor measurement pairs; and determine a function forestimating a blood-glucose concentration in a patient from sensormeasurements based, at least in part, on the estimated one or moreparameters.

Other alternative example embodiments are described herein and/orillustrated in the accompanying Drawings. Additionally, particularexample embodiments may be directed to an article comprising a storagemedium including machine-readable instructions stored thereon which, ifexecuted by a special purpose computing device and/or processor, may bedirected to enable the special purpose computing device/processor toexecute at least a portion of described method(s) according to one ormore particular implementations. In other particular exampleembodiments, a sensor may be adapted to generate one or more signalsresponsive to a measured blood glucose concentration in a body while aspecial purpose computing device/processor may be adapted to perform atleast a portion of described method(s) according to one or moreparticular implementations based upon the one or more signals generatedby the sensor.

BRIEF DESCRIPTION OF THE FIGURES

Non-limiting and non-exhaustive features will be described withreference to the following figures, wherein like reference numeralsrefer to like parts throughout the various figures:

FIG. 1 is a block diagram of an example closed loop glucose controlsystem in accordance with an embodiment.

FIG. 2 is a front view of example closed loop hardware located on a bodyin accordance with an embodiment.

FIG. 3( a) is a perspective view of an example glucose sensor system foruse in accordance with an embodiment.

FIG. 3( b) is a side cross-sectional view of a glucose sensor system ofFIG. 3( a) for an embodiment.

FIG. 3( c) is a perspective view of an example sensor set of a glucosesensor system of FIG. 3( a) for an embodiment.

FIG. 3( d) is a side cross-sectional view of a sensor set of FIG. 3( c)for an embodiment.

FIG. 4 is a cross sectional view of an example sensing end of a sensorset of FIG. 3( d) for an embodiment.

FIG. 5 is a top view of an example infusion device with a reservoir doorin an open position, for use according to an embodiment.

FIG. 6 is a side view of an example infusion set with an insertionneedle pulled out, for use according to an embodiment.

FIG. 7 is a cross-sectional view of an example sensor set and an exampleinfusion set attached to a body in accordance with an embodiment.

FIG. 8( a) is a diagram of an example single device and its componentsfor a glucose control system in accordance with an embodiment.

FIG. 8( b) is a diagram of two example devices and their components fora glucose control system in accordance with an embodiment.

FIG. 8( c) is another diagram of two example devices and theircomponents for a glucose control system in accordance with anembodiment.

FIG. 8( d) is a diagram of three example devices and their componentsfor a glucose control system in accordance with an embodiment.

FIG. 9 is a block diagram of an example closed loop system to controlblood glucose levels using a controller, a filter and/or calibrationunit, and/or correction algorithms through insulin infusion based onglucose level feedback in accordance with an embodiment.

FIG. 10( a) is a schematic diagram that illustrates an example ofglucose propagation within tissues of a body in conjunction with asensor in accordance with an embodiment.

FIG. 10( b) is a graphical diagram that illustrates an examplerelationship between glucose that is present in blood and glucose thatis present within interstitial fluid in accordance with an embodiment.

FIG. 11 is a block diagram of an example filter and/or calibration unitthat produces output information based on, input data in accordance withan embodiment.

FIG. 12 is a block diagram of an example filter and calibrationalgorithm unit in accordance with an embodiment.

FIG. 13 is a flow diagram of an example method for calibrating a controlsystem at least partly automatically in accordance with an embodiment.

FIG. 14 is a block diagram that illustrates example phenomena, which mayinclude a time delay and/or noise, that may affect a signal and a filterto at least partially account for such phenomena in accordance with anembodiment.

FIG. 15 is a block diagram that illustrates an example approach fordetermining a time delay and/or noise in accordance with an embodiment.

FIG. 16 depicts example graphical diagrams to illustrate frequencyversus a phase delay and a delay-creating transfer function inaccordance with an embodiment.

FIG. 17 depicts example graphical diagrams to illustrate time versusmultiple signals in accordance with an embodiment.

FIG. 18 depicts example graphical diagrams to illustrate frequencyversus a phase delay and a corrective transfer function in accordancewith an embodiment.

FIG. 19 is a block diagram that illustrates an example estimator andcalibration model unit to determine sensor glucose in accordance with anembodiment.

FIG. 20 is a flow diagram of an example method for online calibration ofsensor measurements using one or more probability models in accordancewith an embodiment.

FIG. 21 is a block diagram that illustrates multiple example estimatorsand calibration model units to determine a composite sensor glucose inaccordance with an embodiment.

DETAILED DESCRIPTION

In an example glucose monitoring sensor and/or insulin delivery systemenvironment, measurements reflecting blood-glucose levels may beemployed in a closed loop infusion system for regulating a rate of fluidinfusion into a body. In particular example embodiments, a sensor and/orsystem may be adapted to regulate a rate of insulin and/or glucagoninfusion into a body of a patient based, at least in part, on a glucoseconcentration measurement taken from a body (e.g., from a blood-glucosesensor, including a current sensor). In certain example implementations,such a system may be designed to model a pancreatic beta cell (β-cell).Here, such a system may control an infusion device to release insulininto a body of a patient in an at least approximately similarconcentration profile as might be created by fully functioning humanβ-cells if such were responding to changes in blood glucoseconcentrations in the body. Thus, such a closed loop infusion system maysimulate a body's natural insulin response to blood glucose levels.Moreover, it may not only make efficient use of insulin, but it may alsoaccount for other bodily functions as well because insulin can have bothmetabolic and mitogenic effects.

According to certain embodiments, examples of closed-loop systems asdescribed herein may be implemented in a hospital environment to monitorand/or control levels of glucose and/or insulin in a patient. Here, aspart of a hospital or other medical facility procedure, a caretaker orattendant may be tasked with interacting with a closed-loop system to,for example: enter blood-glucose reference measurement samples intocontrol equipment to calibrate blood glucose measurements obtained fromblood-glucose sensors, make manual adjustments to devices, and/or makechanges to therapies, just to name a few examples. Alternatively,according to certain embodiments, examples of closed-loop systems asdescribed herein may be implemented in non-hospital environments tomonitor and/or control levels of glucose and/or insulin in a patient.Here, a patient or other non-medical professional may be responsible forinteracting with a closed-loop system.

FIG. 1 is a block diagram of an example closed loop glucose controlsystem in accordance with an embodiment. Particular embodiments mayinclude a glucose sensor system 10, a controller 12, an insulin deliverysystem 14, and a glucagon delivery system 15, as shown in FIG. 1. Incertain example embodiments, glucose sensor system 10 may generate asensor signal 16 representative of blood glucose levels 18 in body 20,and it may provide sensor signal 16 to controller 12. Controller 12 mayreceive sensor signal 16 and generate commands 22 that are communicatedto insulin delivery system 14 and/or glucagon delivery system 15.Insulin delivery system 14 may receive commands 22 and infuse insulin 24into body 20 in response to commands 22. Likewise, glucagon deliverysystem 15 may receive commands 22 and infuse glucagon 25 into body 20 inresponse to commands 22.

Glucose sensor system 10 may include a glucose sensor, sensor electricalcomponents to provide power to a sensor and to generate sensor signal16, a sensor communication system to carry sensor signal 16 tocontroller 12, and a sensor system housing for electrical components anda sensor communication system.

Controller 12 may include electrical components and software to generatecommands 22 for insulin delivery system 14 and/or glucagon deliverysystem 15 based on sensor signal 16. Controller 12 may also include acontroller communication system to receive sensor signal 16 and providecommands 22 to insulin delivery system 14 and/or glucagon deliverysystem 15. In particular example implementations, controller 12 mayinclude a user interface and/or operator interface (not shown)comprising a data input device and/or a data output device. Such a dataoutput device may, for example, generate signals to initiate an alarmand/or include a display or printer for showing status of a controller12 and/or a patient's vital indicators. Such a data input device maycomprise dials, buttons, pointing devices, manual switches, alphanumerickeys, a touch-sensitive display, combinations thereof, and/or the likefor receiving user and/or operator inputs. It should be understood,however, that these are merely examples of input and output devices thatmay be a part of an operator and/or user interface and that claimedsubject matter is not limited in these respects.

Insulin delivery system 14 may include an infusion device and/or aninfusion tube to infuse insulin 24 into body 20. Similarly, glucagondelivery system 15 may include an infusion device and/or an infusiontube to infuse glucagon 25 into body 20. In alternative embodiments,insulin 24 and glucagon 25 may be infused into body 20 using a sharedinfusion tube. In other alternative embodiments, insulin 24 and/orglucagon 25 may be infused using an intravenous system for providingfluids to a patient (e.g., in a hospital or other medical environment).When an intravenous system is employed, glucose may be infused directlyinto a bloodstream of a body instead of or in addition to infusingglucagon into interstitial tissue. It should be understood, however,that certain example embodiments may include an insulin delivery system14, such as an insulin delivery system without a glucagon deliverysystem.

In particular embodiments, an infusion device (not explicitly identifiedin FIG. 1) may include infusion electrical components to activate aninfusion motor according to commands 22, an infusion communicationsystem to receive commands 22 from controller 12, and an infusion devicehousing (not shown) to hold the infusion device.

In particular example embodiments, controller 12 may be housed in aninfusion device housing, and an infusion communication system maycomprise an electrical trace or a wire that carries commands 22 fromcontroller 12 to an infusion device. In alternative embodiments,controller 12 may be housed in a sensor system housing, and a sensorcommunication system may comprise an electrical trace or a wire thatcarries sensor signal 16 from sensor electrical components to controllerelectrical components. In other alternative embodiments, controller 12may have its own housing or may be included in a supplemental device. Inyet other alternative embodiments, controller 12 may be co-located withan infusion device and a sensor system within a single housing. Infurther alternative embodiments, a sensor, a controller, and/or infusioncommunication systems may utilize a cable; a wire; a fiber optic line;RF, IR, or ultrasonic transmitters and receivers; combinations thereof;and/or the like instead of electrical traces, just to name a fewexamples.

Overview of Example Systems

FIGS. 2-6 illustrate example glucose control systems in accordance withcertain embodiments. FIG. 2 is a front view of example closed loophardware located on a body in accordance with certain embodiments. FIGS.3( a)-3(d) and 4 show different views and portions of an example glucosesensor system for use in accordance with certain embodiments. FIG. 5 isa top view of an example infusion device with a reservoir door in anopen position in accordance with certain embodiments. FIG. 6 is a sideview of an example infusion set with an insertion needle pulled out inaccordance with certain embodiments.

Particular example embodiments may include a sensor 26, a sensor set 28,a telemetered characteristic monitor 30, a sensor cable 32, an infusiondevice 34, an infusion tube 36, and an infusion set 38, any or all ofwhich may be worn on a body 20 of a user or patient, as shown in FIG. 2.As shown in FIGS. 3( a) and 3(b), telemetered characteristic monitor 30may include a monitor housing 31 that supports a printed circuit board33, battery or batteries 35, antenna (not shown), a sensor cableconnector (not shown), and so forth. A sensing end 40 of sensor 26 mayhave exposed electrodes 42 that may be inserted through skin 46 into asubcutaneous tissue 44 of a user's body 20, as shown in FIGS. 3( d) and4. Electrodes 42 may be in contact with interstitial fluid (ISF) that isusually present throughout subcutaneous tissue 44.

Sensor 26 may be held in place by sensor set 28, which may be adhesivelysecured to a user's skin 46, as shown in FIGS. 3( c) and 3(d). Sensorset 28 may provide for a connector end 27 of sensor 26 to connect to afirst end 29 of sensor cable 32. A second end 37 of sensor cable 32 mayconnect to monitor housing 31. Batteries 35 that may be included inmonitor housing 31 provide power for sensor 26 and electrical components39 on printed circuit board 33. Electrical components 39 may samplesensor signal 16 (e.g., of FIG. 1) and store digital sensor values(Dsig) in a memory. Digital sensor values Dsig may be periodicallytransmitted from a memory to controller 12, which may be included in aninfusion device.

With reference to FIGS. 2 and 5 (and FIG. 1), a controller 12 mayprocess digital sensor values Dsig and generate commands 22 (e.g., ofFIG. 1) for infusion device 34. Infusion device 34 may respond tocommands 22 and actuate a plunger 48 that forces insulin 24 (e.g., ofFIG. 1) out of a reservoir 50 that is located inside an infusion device34. Glucose may be infused from a reservoir responsive to commands 22using a similar and/or analogous device (not shown). In alternativeimplementations, glucose may be administered to a patient orally.

In particular example embodiments, a connector tip 54 of reservoir 50may extend through infusion device housing 52, and a first end 51 ofinfusion tube 36 may be attached to connector tip 54. A second end 53 ofinfusion tube 36 may connect to infusion set 38 (e.g., of FIGS. 2 and6). With reference to FIG. 6 (and FIG. 1), insulin 24 (e.g., of FIG. 1)may be forced through infusion tube 36 into infusion set 38 and intobody 16 (e.g., of FIG. 1). Infusion set 38 may be adhesively attached toa user's skin 46. As part of infusion set 38, a cannula 56 may extendthrough skin 46 and terminate in subcutaneous tissue 44 to completefluid communication between a reservoir 50 (e.g., of FIG. 5) andsubcutaneous tissue 44 of a user's body 16.

In example alternative embodiments, as pointed out above, a closed-loopsystem in particular implementations may be a part of a hospital-basedglucose management system. Given that insulin therapy during intensivecare has been shown to dramatically improve wound healing and reduceblood stream infections, renal failure, and polyneuropathy mortality,irrespective of whether subjects previously had diabetes (See, e.g., Vanden Berghe G. et al. NEJM 345: 1359-67, 2001), particular exampleimplementations may be used in a hospital setting to control a bloodglucose level of a patient in intensive care. In such alternativeembodiments, because an intravenous (IV) hookup may be implanted into apatient's arm while the patient is in an intensive care setting (e.g.,ICU), a closed loop glucose control may be established that piggy-backsoff an existing IV connection. Thus, in a hospital or othermedical-facility based system, IV catheters that are directly connectedto a patient's vascular system for purposes of quickly delivering IVfluids, may also be used to facilitate blood sampling and directinfusion of substances (e.g., insulin, glucose, anticoagulants, etc.)into an intra-vascular space.

Moreover, glucose sensors may be inserted through an IV line to provide,e.g., real-time glucose levels from the blood stream. Therefore,depending on a type of hospital or other medical-facility based system,such alternative embodiments may not necessarily utilize all of thedescribed system components. Examples of components that may be omittedinclude, but are not limited to, sensor 26, sensor set 28, telemeteredcharacteristic monitor 30, sensor cable 32, infusion tube 36, infusionset 38, and so forth. Instead, standard blood glucose meters and/orvascular glucose sensors, such as those described in co-pending U.S.Patent Application Publication No. 2008/0221509 (U.S. patent applicationSer. No. 12/121,647; to Gottlieb, Rebecca et al.; entitled “MULTILUMENCATHETER”), filed 15 May 2008, may be used to provide blood glucosevalues to an infusion pump control, and an existing IV connection may beused to administer insulin to an patient. Other alternative embodimentsmay also include fewer, more, and/or different components than thosethat are described herein and/or illustrated in the accompanyingDrawings.

Example System and/or Environmental Delays

Example system and/or environmental delays are described herein.Ideally, a sensor and associated component(s) would be capable ofproviding a real time, noise-free measurement of a parameter, such as ablood glucose measurement, that a control system is intended to control.However, in real-world implementations, there are typicallyphysiological, chemical, electrical, algorithmic, and/or other sourcesof time delays that cause a sensor measurement to lag behind an actualpresent value. Also, as noted herein, such a delay may arise from, forinstance, a particular level of noise filtering that is applied to asensor signal.

FIG. 7 is a cross-sectional view of an example sensor set and an exampleinfusion set that is attached to a body in accordance with anembodiment. In particular example implementations, as shown in FIG. 7, aphysiological delay may arise from a time that transpires while glucosemoves between blood plasma 420 and interstitial fluid (ISF). Thisexample delay may be represented by a circled double-headed arrow 422.As discussed above with reference to FIG. 2-6, a sensor may be insertedinto subcutaneous tissue 44 of body 20 such that electrode(s) 42 (e.g.,of FIGS. 3 and 4) near a tip, or sending end 40, of sensor 26 are incontact with ISF. However, a parameter to be measured may include aconcentration of glucose in blood.

Glucose may be carried throughout a body in blood plasma 420. Through aprocess of diffusion, glucose may move from blood plasma 420 into ISF ofsubcutaneous tissue 44 and vice versa. As blood glucose level 18 (e.g.,of FIG. 1) changes, so does a glucose level of ISF. However, a glucoselevel of ISF may lag behind blood glucose level 18 due to a timerequired for a body to achieve glucose concentration equilibrium betweenblood plasma 420 and ISF. Some studies have shown that glucose lag timesbetween blood plasma and ISF may vary between, e.g., 0 to 30 minutes.Some parameters that may affect such a glucose lag time between bloodplasma and ISF are an individual's metabolism, a current blood glucoselevel, whether a glucose level is rising or falling, combinationsthereof, and so forth, just to name a few examples.

A chemical reaction delay 424 may be introduced by sensor responsetimes, as represented by a circle 424 that surrounds a tip of sensor 26in FIG. 7. Sensor electrodes 42 (e.g., of FIGS. 3 and 4) may be coatedwith protective membranes that keep electrodes 42 wetted with ISF,attenuate the glucose concentration, and reduce glucose concentrationfluctuations on an electrode surface. As glucose levels change, suchprotective membranes may slow the rate of glucose exchange between ISFand an electrode surface. In addition, there may be chemical reactiondelay(s) due to a reaction time for glucose to react with glucoseoxidase GOX to generate hydrogen peroxide and a reaction time for asecondary reaction, such as a reduction of hydrogen peroxide to water,oxygen, and free electrons.

Thus, an insulin delivery delay may be caused by a diffusion delay,which may be a time for insulin that has been infused into a tissue todiffuse into the blood stream. Other contributors to insulin deliverydelay may include, but are not limited to: a time for a delivery systemto deliver insulin to a body after receiving a command to infuseinsulin; a time for insulin to spread throughout a circulatory systemonce it has entered the blood stream; and/or by other mechanical,electrical/electronic, or physiological causes alone or in combination,just to name a few examples. In addition, a body clears insulin evenwhile an insulin dose is being delivered from an insulin delivery systeminto the body. Because insulin is continuously cleared from blood plasmaby a body, an insulin dose that is delivered to blood plasma too slowlyor is delayed is at least partially, and possibly significantly, clearedbefore the entire insulin dose fully reaches blood plasma. Therefore, aninsulin concentration profile in blood plasma may never achieve a givenpeak (nor follow a given profile) that it may have achieved if therewere no delay.

Moreover, there may also be a processing delay as an analog sensorsignal Isig is converted to digital sensor values Dsig. In particularexample embodiments, an analog sensor signal Isig may be integrated overone-minute intervals and converted to a number of counts. Thus, in sucha case, an analog-to-digital (A/D) conversion time may result in anaverage delay of 30 seconds. In particular example embodiments,one-minute values may be averaged into 5-minute values before they areprovided to controller 12 (e.g., of FIG. 1). A resulting average delaymay be two-and-one-half minutes (e.g., half of the averaging interval).In example alternative embodiments, longer or shorter integration timesmay be used that result in longer or shorter delay times.

In other example embodiments, an analog sensor signal current Isig maybe continuously converted to an analog voltage Vsig, and an A/Dconverter may sample voltage Vsig every 10 seconds. Thus, in such acase, six 10-second values may be pre-filtered and averaged to create aone-minute value. Also, five one-minute values may be filtered andaveraged to create a five-minute value that results in an average delayof two-and-one-half minutes. In other alternative embodiments, othersensor signals from other types of sensors may be converted to digitalsensor values Dsig as appropriate before transmitting the digital sensorvalues Dsig to another device. Moreover, other embodiments may use otherelectrical components, other sampling rates, other conversions, otherdelay periods, a combination thereof, and so forth.

System Configuration Examples

FIG. 8( a)-8(d) illustrate example diagrams of one or more devices andtheir components for glucose control systems in accordance with certainembodiments. These FIG. 8( a)-8(d) show exemplary, but not limiting,illustrations of components that may be utilized with certaincontroller(s) that are described herein above. Various changes incomponents, layouts of such components, combinations of elements, and soforth may be made without departing from the scope of claimed subjectmatter.

Before it is provided as an input to controller 12 (e.g., of FIG. 1), asensor signal 16 may be subjected to signal conditioning such aspre-filtering, filtering, calibrating, and so forth, just to name a fewexamples. Components such as a pre-filter, one or more filters, acalibrator, controller 12, etc. may be separately partitioned orphysically located together (e.g., as shown in FIG. 8( a)), and they maybe included with a telemetered characteristic monitor transmitter 30, aninfusion device 34, a supplemental device, and so forth.

In particular example embodiments, a pre-filter, filter(s), and acalibrator may be included as part of telemetered characteristic monitortransmitter 30, and a controller (e.g., controller 12) may be includedwith infusion device 34, as shown in FIG. 8( b). In example alternativeembodiments, a pre-filter may be included with telemeteredcharacteristic monitor transmitter 30, and a filter and calibrator maybe included with a controller in an infusion device, as shown in FIG. 8(c). In other alternative example embodiments, a pre-filter may beincluded with telemetered characteristic monitor transmitter 30, whilefilter(s) and a calibrator are included in supplemental device 41, and acontroller may be included in the infusion device, as shown in FIG. 8(d).

In particular example embodiments, a sensor system may generate amessage that includes information based on a sensor signal such asdigital sensor values, pre-filtered digital sensor values, filtereddigital sensor values, calibrated digital sensor values, commands, andso forth, just to name a few examples. Such a message may include othertypes of information as well, including, by way of example but notlimitation, a serial number, an ID code, a check value, values for othersensed parameters, diagnostic signals, other signals, and so forth. Inparticular example embodiments, digital sensor values Dsig may befiltered in a telemetered characteristic monitor transmitter 30, andfiltered digital sensor values may be included in a message sent toinfusion device 34 where the filtered digital sensor values may becalibrated and used in a controller. In other example embodiments,digital sensor values Dsig may be filtered and calibrated beforetransmission to a controller in infusion device 34. Alternatively,digital sensor values Dsig may be filtered, calibrated, and used in acontroller to generate commands 22 that are sent from telemeteredcharacteristic monitor transmitter 30 to infusion device 34.

In further example embodiments, additional components, such as apost-calibration filter, a display, a recorder, a blood glucose meter,etc. may be included in devices with any of the other components, orthey may stand-alone. If a blood glucose meter is built into a device,for instance, it may be co-located in the same device that contains acalibrator. In alternative example embodiments, more, fewer, and/ordifferent components may be implemented than those that are shown inFIG. 8 and/or described herein above.

In particular example embodiments, RF telemetry may be used tocommunicate between devices that contain one or more components, such astelemetered characteristic monitor transmitter 30 and infusion device34. In alternative example embodiments, other communication mediums maybe employed between devices, such as wireless wide area network (WAN)(e.g., cell communication), Wi-Fi, wires, cables, IR signals, lasersignals, fiber optics, ultrasonic signals, and so forth, just to name afew examples.

Example Approaches to Calibrating Glucose and/or Insulin Systems

FIG. 9 is a block diagram of an example closed loop system to controlblood glucose levels using a controller, a filter and/or calibrationunit, and/or correction algorithms through insulin infusion based onglucose level feedback in accordance with an embodiment. In particularexample embodiments, a closed loop control system may be used fordelivering insulin to a body to compensate for β-cells that performinadequately. There may be a desired basal blood glucose level G_(B) fora particular body. A difference between a desired basal blood glucoselevel G_(B) and an estimate of a present blood glucose level G is theglucose level error G_(E) that may be corrected. For particular exampleembodiments, glucose level error G_(E) may be provided as an input tocontroller 12, as shown in FIG. 9. Although controller 12 may berealized as a proportional-integral-derivative (PID) controller, claimedsubject matter is not so limited, and controller 12 may be realized inalternative manners.

If glucose level error G_(E) is positive (meaning, e.g., that a presentestimate of blood glucose level G is higher than a desired basal bloodglucose level G_(B)), then a command from controller 12 may generate acommand 22 to drive insulin delivery system 34 to provide insulin 24 tobody 20. Insulin delivery system 34 may be an example implementation ofinsulin delivery system 14 (e.g., of FIG. 1). Likewise, if G_(E) isnegative (meaning, e.g., that a present estimate of blood glucose levelG is lower than a desired basal blood glucose level G_(B)), then acommand from controller 12 may generate a command 22 to drive glucagondelivery system 35 to provide glucagon 25 to body 20. Glucagon deliverysystem 35 may be an example implementation of glucagon delivery system15 (e.g., of FIG. 1).

In terms of a control loop for purposes of discussion, glucose may beconsidered to be positive, and therefore insulin may be considered to benegative. Sensor 26 may sense an ISF glucose level of body 20 andgenerate a sensor signal 16. For certain example embodiments, a controlloop may include a filter and/or calibration unit 456 and/or correctionalgorithm(s) 454. However, this is by way of example only, and claimedsubject matter is not so limited. Sensor signal 16 may be filtered/orand calibrated at unit 456 to create an estimate of present bloodglucose level 452. In certain example embodiments that are describedherein with particular reference to FIGS. 10( a)-21, filtering and/orcalibrating may be performed by filter and/or calibration unit 456.Although shown separately, filter and/or calibration unit 456 may beintegrated with controller 12 without departing from claimed subjectmatter. Moreover, filter and/or calibration unit 456 may alternativelybe realized as part of controller 12 (or vice versa) without departingfrom claimed subject matter.

In particular example embodiments, an estimate of present blood glucoselevel G may be adjusted with correction algorithms 454 before it iscompared to a desired basal blood glucose level G₈ to calculate a newglucose level error G_(E) to start a loop again. Also, an attendant, acaretaker, a patient, etc. may obtain blood glucose reference samplemeasurements from a patient's blood using, e.g., glucose test strips.These blood-based sample measurements may be used to calibrate ISF-basedsensor measurements using techniques, e.g., such as those described inU.S. Pat. No. 6,895,263, issued 17 May 2005, separately or inconjunction with the (e.g., calibration-related) principles that aredescribed herein.

For an example PID-type of controller 12, if a glucose level error G_(E)is negative (meaning, e.g., that a present estimate of blood glucoselevel is lower than a desired basal blood glucose level G_(B)), thencontroller 12 may reduce or stop insulin delivery depending on whetheran integral component response of a glucose error G_(E) is stillpositive. In alternative embodiments, as discussed below, controller 12may initiate infusion of glucagon 25 if glucose level error G_(E) isnegative. If a glucose level error G_(E) is zero (meaning, e.g., that apresent estimate of blood glucose level is equal to a desired basalblood glucose level G_(B)), then controller 12 may or may not issuecommands to infuse insulin 24 or glucagon 25, depending on a derivativecomponent (e.g., whether glucose level is raising or falling) and/or anintegral component (e.g., how long and by how much glucose level hasbeen above or below basal blood glucose level G_(B)).

To more clearly understand the effects that a body has on such a controlloop, a more detailed description of physiological effects that insulinhas on glucose concentration in ISF is provided. In particular exampleembodiments, infusion delivery system 34 delivers insulin into ISF ofsubcutaneous tissue 44 (e.g., also of FIGS. 3, 4, and 6) of body 20.Alternatively, insulin delivery system 34 or a separate infusion device(e.g., glucagon delivery system 35) may similarly deliver glucose intoISF of subcutaneous tissue 44. Here, insulin may diffuse from local ISFsurrounding a cannula into blood plasma and spread throughout body 20 ina main circulatory system. Infused insulin may diffuse from blood plasmainto ISF substantially throughout the entire body.

Here in the body, insulin 24 may bind with and activate membranereceptor proteins on cells of body tissues. This may facilitate glucosepermeation into activated cells. In this way, tissues of body 20 maytake up glucose from ISF. As ISF glucose level decreases, glucose maydiffuse from blood plasma into ISF to maintain glucose concentrationequilibrium. Glucose in ISF may permeate a sensor membrane of sensor 26and affect sensor signal 16. Propagation of glucose throughout a body isdescribed further herein below with particular reference to FIG. 10( a).

In addition, insulin may have direct and indirect effects on liverglucose production. Typically, increased insulin concentration maydecrease liver glucose production. Therefore, acute and immediateinsulin response may not only help a body to efficiently take upglucose, but it may also substantially stop a liver from adding toglucose in the blood stream. In alternative example embodiments, aspointed out above, insulin and/or glucose may be delivered more directlyinto the blood stream instead of into ISF, such as by delivery intoveins, arteries, the peritoneal cavity, and so forth, just to name a fewexamples. Accordingly, any time delay associated with moving insulinand/or glucose from ISF into blood plasma may be diminished. In otheralternative example embodiments, a glucose sensor may be in contact withblood or other body fluids instead of ISF, or a glucose sensor may beoutside of a body such that it may measure glucose through anon-invasive means. Embodiments using alternative glucose sensors mayhave shorter or longer delays between an actual blood glucose level anda measured blood glucose level.

One or more controller gains may be selected so that commands from acontroller 12 direct infusion device 34 to release insulin 24 into body20 at a particular rate. Such a particular rate may cause insulinconcentration in blood to follow a similar concentration profile aswould be caused by fully functioning human β-cells responding to bloodglucose concentrations in a body. Similarly, controller gain(s) may beselected so that commands from controller 12 direct infusion device 35to release glucagon 25 in response to insulin excursions. In particularexample embodiments, controller gains may be selected at least partiallyby observing insulin response(s) of several normal glucose tolerant(NGT) individuals having healthy, normally-functioning β-cells.

In one or more example implementations, a system may additionallyinclude a communication unit 458. A communication unit 458 may comprise,by way of example but not limitation, a wireless wide area communicationmodule (e.g., a cell modem), a transmitter and/or a receiver (e.g., atransceiver), a Wi-Fi chip or radio, some combination thereof, and soforth. Communication unit 458 may receive signals from filter and/orcalibration unit 456 and/or from sensor 26 (e.g., sensor signal 16).Although not specifically shown in FIG. 9, communication unit 458 mayalso receive signals from other units (e.g., controller 12). Also,communication unit 458 may be capable of providing signals to any of theother units of FIG. 9 (e.g., controller 12, filter and/or calibrationunit 456, etc.). Communication unit 458 may also be integrated with orotherwise form a part of another unit, such as controller 12 or filterand/or calibration unit 456.

Communication unit 458 may be capable of transmitting calibrationoutput, calibration failure alarms, control algorithms state, and otherphysiological, hardware, and/or software data (e.g., diagnostic data),etc. to a remote data center for additional processing and/or storage(e.g., for remote telemetry purposes). These transmissions can beperformed automatically, semi-automatically (e.g., at the request of theremote data center), and/or manually at the request of the patient, andso forth, just to provide a few examples. The data can be subsequentlyserved on request to remote clients including, but not limited to,mobile phones, physician's workstations, patient's desktop computers,any combination of the above, and so forth, just to name a few examples.Communication unit 458 may also be capable of receiving from a remotelocation various information, including but not limited to: calibrationinformation, instructions, other control information, some combinationthereof, and so forth. Such control information may be provided fromcommunication unit 458 to other system unit(s) (e.g., controller 12,filter and/or calibration unit 456, etc.).

A glucose and/or insulin control system may be calibrated relativelyconstantly, at intervals, regularly, occasionally, upon request, atother specified or random times, some combination thereof, and so forth.A continuous glucose measuring sensor (CGMS), for example, may detect aglucose concentration in ISF and transmit a proportional current signal.A current signal (isig) may be linearly correlated with a referenceblood glucose concentration (BG). Hence, a linear model, with twoparameters (e.g., slope and offset), may be used to calculate a sensorglucose concentration (SG) from sensor current isig. In order toaccurately measure SG, parameters of such a linear model may beperiodically calibrated by obtaining BG sample measurements via a BGmeter or YSI.

Calibration may be performed by employing, for example, any one or moreof multiple techniques. Five different example techniques are describedbelow. First, a Bayesian (B) method that uses a Moving Chain Monte Carlo(MCMC) algorithm may be employed. This is a relatively robust technique,especially if/when significant process and/or measurement noises arepresent. However, an MCMC algorithm is typically relatively morecomputationally intensive. A second technique may use a linear KalmanFilter (KF). A KF technique may be relatively accurate in estimatingmodel parameters, and it is also usually less computationally intensivethan a Bayesian method. However, a KF technique may reflect measurementnoise more in estimated parameters.

A third technique may use a linear Kalman Filter with an adaptiveprocess noise matrix (KFQ). Unlike a KF technique, a KFQ method mayupdate a process noise matrix proportional to a bias (e.g., a differencebetween an estimated value and a true or reference value) of the modelif/when a BG sample measurement is available. Therefore, when a bias islarge, the gains calculated by KFQ in order to update model parametersare likely to be more conservative (e.g., smaller). Hence, parameterscan be updated proportionally based on the performance of the model. Afourth technique may use a linear Kalman Filter with an adaptivemeasurement noise matrix (KFR), Unlike a KF technique, a KFR method mayupdate a measurement noise matrix proportional to an error between anestimated value and a true value. Like a KFQ technique, when an error islarge, gains calculated by KFR in order to update model parameters maybe more conservative. A fifth technique may include estimating one ormore parameters using a linear Kalman Filter in which both process noiseand measurement noise are adapted as a function of model performance.Any two or more, all five of these, and/or other techniques may be usedin parallel. If used in parallel, a weighting scheme may be assigned toeach technique based on its respective performance. Based on theweights, a combined SG value may be calculated from two or more of thefive example methods. These five techniques are described herein belowwith particular reference to FIGS. 19-21. Although five methods aredescribed herein by way of example, claimed subject matter is not solimited, and other technique(s) may alternatively be implemented.

As is described herein above with particular reference to FIG. 7 andfurther herein below with particular reference to FIG. 10( a), there istypically a time lag between blood glucose and interstitial fluid (ISF)glucose. Such a time lag can decrease an accuracy of real-timecontinuous glucose monitoring (CGM) systems that use subcutaneoussensors (or other sensors that do not directly sense glucose in theblood). Each ISF glucose value measured in real-time may be laggingblood glucose (BG) by the sum of this time lag plus any inherentelectrochemical sensor delay due to the reaction process, as well as anyfront-end signal processing delays incurred to produce smooth traces.Furthermore, this time lag can create ambiguity in a sensor calibrationprocess because this lag can appear as an offset normally attributed tobackground current.

Accurately correcting for this time lag can improve overall performance,and it can potentially at least reduce sources of variance incurredwhile calibrating, especially on fast rising and/or falling glucoseexcursions. Such time lag correction can also provide a user with arelatively instantaneous BG values with latent periods removed. This canbe particularly relevant in the context of closed loop devices in whichinsulin infusions may be changed relatively frequently (e.g., on a perminute basis).

For particular example implementations(s), algorithms are described thatinclude an inverse filter with noise reduction properties, which isknown as a Wiener filter, to correct for a determinable time lag whileperforming a degree of smoothing. Certain example embodiments maycalculate a time delay between ISF glucose and plasma glucose, measure anoise level of a sensor signal, and/or use these two components todevelop in real-time a time lag correction and noise reduction filter. Afilter residual may be calibrated using any of a number of algorithms(e.g., linear regression, etc.).

Example dynamics for plasma and ISF glucose are described with referenceto FIG. 10( a). More specifically, an example two compartment model isillustrated in FIG. 10( a). It may be used to represent a dynamicrelationship between ISF glucose and plasma glucose. FIG. 10( a) is aschematic diagram 1000 that illustrates an example of glucosepropagation within tissues of a body in conjunction with a sensor inaccordance with an embodiment. FIG. 10( a) also relates to FIG. 7, whichis described herein above.

Diagram 1000 includes a capillary 1002 (or, more generally, a bloodvessel), ISF 1004, and a fat and/or muscle cell 1006. With reference toFIG. 9, these portions may relate to body 20. More specifically,capillary 1002 (e.g., which may contain blood plasma 420 of FIG. 7) maycorrespond to a blood stream, and ISF 1004 may correspond to, forexample, subcutaneous tissue 44 (e.g., of FIGS. 7 and 9). Diagram 1000also includes a sensor 1008. Sensor 1008 may correspond to sensor 26(e.g., of FIGS. 7 and 9). Although shown in FIG. 10( a) and generallydescribed herein as a subcutaneous current sensor placed within ISF,sensor 1008 may alternatively indirectly sense blood glucose fromanother portion of a body without departing from claimed subject matter.Plasma of capillary 1002 has a volume of V₁. ISF 1004 has a volume ofV₂. Diagram 1000 further indicates two concentrations C₁ and C₂ andthree rates of propagation k₀₂, k₁₂, and k₂₁.

A two compartment model is based on an assumption, without loss ofgenerality or limitation, that a capillary 1002 separating plasma andISF 1004 compartments creates a resistance to glucose diffusion fromplasma space into ISF space. Glucose may be cleared from ISF space by arate proportional to, for example, a concentration of glucose in thatcompartment. An example mathematical relationship is represented by thefollowing mass balance equation:

$\begin{matrix}{\frac{C_{2}}{t} = {{{- \left( {k_{02} + k_{12}} \right)}C_{2}} + {k_{21}\frac{V_{1}}{V_{2}}C_{1}}}} & (1)\end{matrix}$

where a rate of glucose clearance from subcutaneous tissue has aconstant uptake rate of k₀₂, and constant glucose diffusion ratesbetween plasma and subcutaneous tissue of k₁₂ and k₂₁, respectively.Plasma and ISF compartments have concentrations C₁ and C₂ andcorresponding volumes V₁ and V₂, respectively.

A plasma-to-ISF time constant and gradient can be expressed as

$\begin{matrix}{{\frac{C_{2}}{C_{1}} = {\frac{k_{21}}{k_{12} + k_{02}} \cdot \frac{V_{1}}{V_{2}}}},\mspace{14mu} {\tau = \frac{1}{k_{12} + k_{02}}}} & (2)\end{matrix}$

where time constant τ is the time delay between plasma and ISF glucose.Equation (2) assumes, without loss of generality or limitation, steadystate conditions in which a steady state glucose concentration in an ISFcompartment (C₂) may be dependent upon a rate of glucose clearance fromthis compartment (k₀₂) and a rate of glucose diffusion to thecompartment (k₁₂ and k₂₁). Rate parameters are assumed, without loss ofgenerality or limitation, to be constant; consequently, a time lagbetween ISF glucose and plasma glucose concentration may also beconstant, as well may be the gradient thereof too.

FIG. 10( b) is a graphical diagram 1050 that illustrates an examplerelationship between glucose that is present in blood and glucose thatis present within ISF in accordance with an embodiment. A theoreticalplasma glucose step response 1052 is illustrated in diagram 1050 with anexample resulting ISF glucose concentration 1054 superimposed for aunity gradient and first order time lag of 10 minutes. For such anexample, it may take approximately 50 minutes or 5 time constants forthe transient response from ISF glucose concentration to completelyequilibrate.

As illustrated in FIG. 10( a), plasma glucose may be estimated from ameasurement of ISF glucose through an electrochemical (e.g., current)sensor. A low current, which is usually in the nano Amp range, may bemeasured through an electrochemical reaction that is considered to beproportional to ISF glucose. These sensors and/or measurements therefrom may be subsequently calibrated via a BG sample measured with theuse of a BG monitor.

FIG. 11 is a block diagram 1100 of an example filter and/or calibrationunit 456 that produces output information 1112 based on input data 1110in accordance with an embodiment. As illustrated, filter and/orcalibration unit 456 may include one or more processors 1102 and atleast one memory 1104. In certain example embodiments, memory 1104 maystore or otherwise include instructions 1106 and/or sample-measurementdata 1108. Sample-measurement data 1108 may include, by way of examplebut not limitation, blood glucose reference samples measured via a bloodsample, blood glucose sensor measurements, blood glucose sample-sensormeasurement pairs, combinations thereof, and so forth.

In particular example implementations, filter and/or calibration unit456 of FIG. 11 may correspond to filter and/or calibration unit 456 ofFIG. 9. Input data 1110 may include sensor measurements (e.g., from anISF current sensor), blood-glucose reference samples, and so forth, justto name a few examples. Output information 1112 may include a presentblood-glucose concentration estimate based, at least in part, on sensormeasurements; time delay(s); noise; and so forth, just to name a fewexamples.

Current sensor measurements of input data 1110 may correspond to sensorsignal 16 (e.g., of FIGS. 1 and 9) and/or values resulting there from.Blood glucose reference samples may correspond to values acquiredthrough, e.g., a finger stick test. They may be manually orautomatically provided to filter and/or calibration unit 456. Outputinformation 1112 may correspond to a present blood glucose concentrationestimate G 452 (e.g., of FIG. 9) and/or values derived there from.

In certain example embodiments, input data 1110 may be provided tofilter and/or calibration unit 456. Based on input data 1110, filterand/or calibration unit 456 may produce output information 1112. Currentsensor measurements and/or blood glucose reference samples that arereceived as input data 1110 may be stored as sample-measurement data1108.

Filter and/or calibration unit 456 may be programmed with instructions1106 to perform algorithms, functions, methods, etc.; to implementattributes, features, etc.; and so forth that are described herein.Filter and/or calibration unit 456 may therefore be coupled to at leastone blood glucose sensor to receive one or more signals based on bloodglucose sensor measurements.

A filter and/or calibration unit 456 that comprises one or moreprocessors 1102 may execute instructions 1106 to thereby render the unita special purpose computing device to perform algorithms, functions,methods, etc.; to implement attributes, features, etc.; and so forththat are described herein. Processor(s) 1102 may be realized asmicroprocessors, digital signal processors (DSPs), application specificintegrated circuits (ASICs), programmable logic devices (PLDs),controllers, micro-controllers, a combination thereof, and so forth,just to name a few examples. Alternatively, an article may comprise atleast one storage medium (e.g., such as memory) having stored thereoninstructions 1106 that are executable by one or more processors.

FIG. 12 is a block diagram of an example filter and calibrationalgorithm unit 1200 in accordance with an embodiment. As illustrated,filter and calibration algorithm unit 1200 may include a filter 1202 anda calibration algorithm 1204. For certain example embodiments, blocks1202 and 1204 jointly show an example process for calibrating a rawsensor signal. A sensor current sample x(n) may be provided to filter1202, which may produce an output signal 1206. Output signal 1206 andblood glucose reference samples (BG) may be provided to calibrationalgorithm 1204. Calibration algorithm 1204 may produce sensor glucoseSG(n), which may correspond to blood glucose measurement values derivedfrom sensor glucose measurements.

Filter 1202, as represented by G(z), may perform time lag correctionand/or noise smoothing (e.g., using a Wiener and/or other filter). Afilter residual output signal 1206 may be paired with BG samples, whichmay be acquired at approximately the same time, for calibration atcalibration algorithm unit 1204. A resulting time-lag and noise-reducedsignal may be calibrated by, e.g., linear regression at calibrationalgorithm unit 1204.

FIG. 13 is a flow diagram 1300 of an example method for calibrating acontrol system at least partly automatically in accordance with anembodiment. As illustrated, flow diagram 1300 includes two operations1302-1304. For certain example embodiments, at operation 1302, bloodglucose reference samples may be correlated with sensor measurements toprovide at least one output signal responsive to a delay associated withthe sensor measurements. Such a correlation may be performed, forexample, by a filter 1202. An example approach to determining a delay isdescribed further herein below with particular reference to FIG. 15.

At operation 1304, a function for estimating a blood-glucoseconcentration in a patient from sensor measurements may be determinedbased, at least in part, on the at least one output signal. Such afunction determination may be performed, for example, by calibrationalgorithm unit 1204. Although a certain number of operations arespecifically illustrated in each flow diagram that is described herein,other embodiments may have a different number and/or differentoperations without departing from claimed subject matter.

FIG. 14 is a block diagram of a model 1400 that illustrates (i) examplephenomena, which may include a time delay and/or noise, that may affecta signal and (ii) a filter 1202 to at least partially account for suchphenomena in accordance with an embodiment. As illustrated, examplemodel 1400 includes a filter 1402 and a filter 1202, as well as a summerZ. Model 1400 illustrates example phenomena, such as a delay θ and/ornoise a, that may distort or otherwise affect a desired signal s(n).Desired signal s(n) may represent blood glucose within a blood stream.When these phenomena impact a desired signal s(n), a measured signalx(n) may result.

For a model of certain example embodiments, a desired signal s(n) may beimpacted by a delay θ, which effect may be modeled by filter H(z) 1402.This delay θ may represent a time lag introduced as glucose diffusesacross a capillary boundary. Filter 1402 produces signal y(n). Noise,such as that caused by a sensor mechanism, is added to signal y(n) asa(n) to produce signal x(n), which may be a signal that is measured by asensor. Filter G(z) 1202 attempts to account for delay θ and/or noise σ.By accommodating one or more of these phenomena, if a measured signalx(n) is input to filter 1202, a facsimile of desired signal s′(n) may berecreated.

In order to accommodate one or more of these phenomena, each phenomenonmay be estimated. In order to approximate a delay between ISF glucoseand plasma glucose, one or more correlations may be performed, forexample. To perform one or more correlations, a matched filter, forexample, may be employed. An example matched filter is shown in FIG. 15.

FIG. 15 is a block diagram 1500 that illustrates an example approach todetermining a time delay θ and/or noise σ in accordance with anembodiment. As illustrated, block diagram 1500 may include a lowpassfilter 1502, a matched filter 1504, a filter delay 1506, and a combiner1508. Measured signal x(n) may be provided to lowpass filter 1502,matched filter 1504, and filter delay 1506. Combiner 1508 may receiveoutput signals from lowpass filter 1502 and filter delay 1506 andproduce noise σ(n).

In certain example embodiments, block diagram 1500 may also includemultiple blood glucose samples 1510 (BG₁, BG₂, BG₃ . . . BG_(n)).Matched filter 1504 may use a number of recent BG measurements for a BGsample template 1510 (e.g., n≧4). These BG sample measurements may bematched to a range of sensor measurements. By way of example, BG samplemeasurements may be paired with sensor measurements that are acquired atapproximately the same time (e.g., during the same minute as the BGsample values) (e.g., t≈0). The sensor sequence of measurements may betime shifted by one sample (e.g., which may be one minute ahead intime), and sensor measurements may be paired with the template BGsamples. This may be repeated for some number (e.g., 20) time shifts.

Each group of BG sample-sensor measurement pairs may be cross-correlatedto measure a correlation for each time lag under analysis. The segmentpair with the greatest correlation or highest magnitude output may beconsidered to represent a delay between current sensor and meter, or ISFand BG. This delay θ may be produced by matched filter 1504. A delay maybe used to create a first order time lag for filter H(z) 1402, as shownin FIG. 14. An example matched filter may be represented by Equation(3):

$\begin{matrix}{{y(i)} = {\sum\limits_{k = 0}^{N - 1}{{{BG}\left( {N - k - 1} \right)} \cdot {x\left( {i - k} \right)}}}} & (3)\end{matrix}$

where x(i) may be raw measurements of a sensor glucose signal, BG(k) maybe samples of the template or BG measurement samples, N may be a filterlength, and i may be a time shift index. If a BG template and sensorsignal coincide or have a highest correlation, a matched filter outputmay be at a high value (e.g., a maximum) and that corresponding timeindex can be set to equal the time delay θ.

Sensor signal noise σ may be extracted by passing a sensor signal x(n)through a linear low-pass filter 1502 with enough stopband attenuationto remove much, if not most, of the noise. For a linear filter example,a filter-delay 1506 group delay may be half of its model order. Rawsignal x(n) may be delayed by this degree as the filter order may beknown at filter delay 1506, and the result thereof may be subtractedfrom a filtered signal output of lowpass filter 1502 at combiner 1508 toretain sensor noise σ.

This sensor noise signal may be passed to the model of FIG. 14 todevelop filter coefficients (e.g., Wiener filter coefficients) forfilter 1202. Coefficients for a time-lag correction filter (e.g., filter1202) may be created based on diagram 1400 of FIG. 14 and/or usingparameters (e.g., delay θ and/or noise a) determined through theapproach or approaches illustrated in FIG. 15.

Distortion(s) in a signal channel may be ameliorated through channelequalization. A plasma-ISF channel equalization may be performed, forexample, in accordance with the following. Signals that are transmittedthrough various types of mediums often undergo distortions in which areceived signal is degraded or otherwise changed to some extent. Theextent of degradation is governed, at least partially, by the intrinsicproperties of a medium. One type of inverse filter that can be used torecover an original signal from a received signal is a Wiener filter. AWiener filter provides a trade-off between inverse filtering and noisereduction. In case(s) of modeling a relationship between ISF glucose andplasma glucose, a medium may be considered to be a capillary wall thatseparates glucose measured within ISF space from that measured in plasmaspace.

A group of digital filter coefficients may be used to describe adiffusion process across a capillary wall. Block diagram 1400 of FIG. 14outlines an example of such a procedure in which a signal to be obtainedmay be plasma glucose s(n). A signal that is acquired through anelectrochemical biosensor and CGM device may be x(n)—a received ormeasurement signal. Measured signal x(n) may be distorted by one or moreproperties of a medium, e.g. a diffusion process as described above. Insignal processing terms, a diffusion process may be equivalent to orotherwise analogized to convolving a plasma blood glucose signal s(n)with an impulse response h(n), which may represent a corrupting factorof a medium, to produce a degraded signal y(n):

y(n)=h(n)

s(n)  (4)

This kind of dynamic relationship between plasma glucose and ISF glucosemay be understood. An appropriate impulse response is thereforedeterminable and can be modeled by, e.g., an infinite-impulse-response(IIR) filter with a first-order lag. By way of example only, afirst-order time lag may be τ=10 minutes with some gradient α. A timelag, however, may be calculated based on a time index delay determinedvia correlation (e.g., using a matched filter as described herein abovewith particular reference to FIG. 15). In the Laplace transform orS-domain, a model may be described using Equation (5):

$\begin{matrix}{{H(s)} = \frac{\alpha}{{10s} + 1}} & (5)\end{matrix}$

A transfer function in the z-domain may be expressed using Equation (6)below, where T is an example sample interval of one minute and α is afilter gain:

$\begin{matrix}{{H(z)} = {\frac{z\; \alpha}{z - e^{{- T}/10}} = \frac{0.0952}{1 - {0.9048z^{- 1}}}}} & (6)\end{matrix}$

Filter gain α, which may be related to a (relatively constant) gradientbetween glucose concentration in ISF and plasma compartments, may bemodified in the above equation to create a unity gradient, therebyproviding a time lag between compartments and obviating a gradient term.

A received signal may be further degraded by additive white noise σ(n).It can also be further corrupted by a range of artifact(s) to produce aresultant acquired signal x(n):

x(n)=y(n)+σ(n)  (7)

In order to better estimate plasma glucose s(n), a set of optimal filtercoefficients g(n) may be derived that can be used to filter a receivedISF glucose signal and/or denoise additive interference. This mayproduce a usable estimate of plasma glucose as shown by Equation (8) asfollows:

s′(n)=x(n)

g(n)  (8)

FIGS. 16-18 depict graphical diagrams to illustrate example conceptualimplementations in accordance with certain embodiments. Morespecifically, FIG. 16 depicts example graphical diagrams 1600 toillustrate frequency versus a phase delay and a delay-creating transferfunction in accordance with an embodiment. FIG. 17 depicts examplegraphical diagrams 1700 to illustrate time versus multiple signals inaccordance with an embodiment. FIG. 18 depicts example graphicaldiagrams 1800 to illustrate frequency versus a phase delay and acorrective transfer function in accordance with an embodiment.

Example frequency and phase responses of filter H(z) (e.g., of FIG. 14)are illustrated in FIG. 16. An example 10 minute time delay can beidentified at DC or 0 Hz. Glucose changes at a relatively very slowrate, at least in signal processing terms inasmuch as a signal of thistype may be considered to be in the ultra-low frequency (ULF) range.Steady-state glucose may be measured at DC, and fast glucose digressionsare typically observed at under 2-3 cycles per hour. In order to producea group of filter coefficients to correct for ISF time lag, anelectrochemical biosensor signal of 40 hours duration was modified tocreate “ideal” and measurement signals in this example. A referencesignal was sampled every minute and decimated to create an “ideal”plasma glucose signal with a sample time interval of 20 minutes.

This “ideal” signal s(n) is illustrated by a first trace at the top ofFIG. 17. An original biosensor signal with a one minute measurement timeinterval was processed by a diffusion filter H(z) with a first ordertime lag of 10 minutes to produce a time delayed signal y(n), which isillustrated by a second trace in the middle of FIG. 17. To model asensor glucose signal acquired subcutaneously from ISF, Gaussian whitenoise may be added to a glucose signal to create a measurement signalx(n) with a signal-to-noise ratio (SNR) of 10 dB, which is shown by athird trace at the bottom of FIG. 17. Signals s(n) and x(n) may thus beused to create a group (e.g., a set) of filter coefficients (e.g.,Wiener filter coefficients). Although white noise is used in thissimulation example and reflected in the graphical diagrams, actual(estimated) sensor noise may be extracted and used as previouslydescribed herein above with particular reference to FIG. 15.

Although other type(s) of filters may alternatively be used withoutdeviating from claimed subject matter, an example implementation that isdescribed below employs a Wiener filter. More specifically, a Wienerfilter implementation is described in the time-domain. For a so-called“optimal” FIR Wiener filter, the MSE may be the sum of the squares ofthe residuals given as:

$\begin{matrix}{{ɛ = {{\sum\limits_{k = 0}^{N - 1}{{e(n)}^{2}}} = {\sum\limits_{k = 0}^{N - 1}{{{s(n)} - {s^{\prime}(n)}}}^{2}}}},} & (9)\end{matrix}$

which is equivalent to finding solutions to a number of Wiener-Hopfequations, as provided in Equation (10):

$\begin{matrix}{{{\sum\limits_{n = 0}^{p - 1}{{g(n)}{r_{xx}\left( {k - n} \right)}}} = {r_{xy}(k)}},\mspace{14mu} {k = 0},1,{{\ldots \mspace{14mu} p} - 1.}} & (10)\end{matrix}$

In matrix form, Equation (10) may be written instead as shown inEquation (11):

g_(opt)=R_(xx) ⁻¹r_(xy,)  (11)

where R_(xx) may be a Toeplitz matrix of an autocorrelation sequencer_(xx) for a given dataset x:

$\begin{matrix}{{r_{xx} = {\sum\limits_{k = 0}^{N - 1}{{x(k)}{x^{*}\left( {k - n} \right)}}}},} & (12)\end{matrix}$

and r_(xy) may be a cross-correlation sequence as shown in Equation(13):

$\begin{matrix}{r_{xy} = {\sum\limits_{k = 0}^{N - 1}{{s(k)}{{x^{*}\left( {k - n} \right)}.}}}} & (13)\end{matrix}$

A desired signal may be estimated for p observations of a currentsample:

$\begin{matrix}{{{s^{\prime}(n)} = {{\sum\limits_{k = 0}^{p - 1}{{g(k)}{x\left( {n - k} \right)}}} = {g^{h}{x(n)}}}},{g = {\left\lbrack {g_{0},g_{1},\ldots \mspace{14mu},{n - p + 1}} \right\rbrack^{T}.}}} & (14)\end{matrix}$

For real values, an autocorrelation matrix may be symmetrical. In theabove derivation, it is assumed, without loss of generality orlimitation, that autocorrelation and cross-correlation sequences areknown. In a case of correcting for ISF time lag, such sequences may beestimated from data samples. An approach that may be adopted to derivetime domain Wiener filter coefficients is described by Equation (15):

g _(opt)=(Y ^(T) Y)⁻¹ Y ^(T) s  (15)

In the above Equation (15), Y may be a Toeplitz matrix of data samples x(e.g., measurement signal) in which a resultant matrix multiplicationinside the parenthesis may be a time-averaged autocorrelation estimateof an input signal. Its inverse may be multiplied by a time-averagedcross-correlation estimate of both input and desired signals (e.g.,terms outside parenthesis).

This approach may use a complete data block formulation to derive agroup of coefficients. It can be shown to approach a Wiener filter ofEquation (11) as the data block length approaches ∞. A time domainWiener filter may be created for this example by applying measurement(x(n)) and desired (s(n)) signals of FIGS. 14 and 17 to Equation (15). Afrequency response of a Wiener filter (G(z)) is illustrated in an uppergraph of FIG. 18 where filter smoothing properties are apparent with ahigh degree of noise suppression above about 6 cycles/hour. Phasecorrection properties may be seen from a phase response illustrated in alower trace of FIG. 18. A phase correction of 10 minutes is apparent atDC in the lower graph.

For certain example embodiments, methods of calibration for glucosesensor current (Isig) to reflect blood glucose reference sample (BG)values are described. Three example parameters may be estimated. Thesethree example parameters may include a calibration factor (CF), acurrent offset (◯), and a delay (Δ) between subcutaneous glucose andblood glucose. Calibration method(s) may produce results withoutexplicit consideration of a variable delay if a manual BG referencesample measurement is taken during an interval of relatively, if notnearly, constant glucose levels. However, it may enhance calibrationaccuracy if calibration is performed on rapidly changing glucose levels.Because glucose variability can often be encountered without a specificcause in subjects with diabetes, certain implementations of this methodmay help to ease fasting requirements prior to measurement. Certainimplementations may also be used in emergency situations whenre-calibration is desired in spite of a possible ongoing glucoseexcursion.

A relationship formula that may include both BG and Isig is considerednext: BG(t)=CF×(Isig(t+Δ)−◯), where Isig(t+Δ) may be sensor currentmeasured at time “t” plus shift “delta” (Δ). Given that BG may bechanging relatively slowly, “D” may be selected to be a time intervalfor which it is acceptable to assume, without loss of generality orlimitation, that change is relatively gradual. During such a timeinterval D, a smoothed Isig may be approximated as a linear function:Isig=A*t+B, where t is changing from 0 to D. Time interval D may bechosen from, e.g., 5 to 15 minutes, and sensor current may be measured,e.g., every minute. However, other time periods, sampling intervals,frequencies, etc. may alternatively be selected without departing fromclaimed subject matter.

The equation for BG that is presented above may be solved bysubstituting a linear relationship and finding BG estimates forinstances of time from t to t+D. Reverting back to the originalequations where CF and ◯ are unknowns and Δ is fixed, it may be solved.An equation for BG may be solved, for example, in a least-squares senseusing singular value decomposition (e.g., a number of equations is D,and a number of unknowns is two). Repeating this procedure for differentdelays Δ can provide a series of solutions with their correspondingerrors. A solution with a lowest or minimal error (e.g., from a givenseries of solutions) may be chosen in order to estimate a delay Δ.

Certain implementations of this method may be used with a single or withmultiple BG sample measurements by combining series of equations fromdifferent instances of BG observations. Certain implementations of thismethod may be relatively simple and computationally efficient because adimension of matrices may be at most D×D for a single point calibration.

Sensor data may be calibrated while a glucose and/or insulin system isfunctioning (e.g., online calibration of sensor current may beperformed). In certain example embodiments, a continuous glucosemonitoring sensor (CGMS) may output a current signal (isig, nAmps),which may be considered to linearly correlate with blood glucoseconcentration (BG, mg/dL). Hence, a linear calibration model may be usedto calculate a sensor glucose concentration (SG, mg/dL) from isig, asshown below in Equations (16) and (17):

SG(t)=CF×isig(t)−CFOS  (16)

CFOS=CF×OS  (17)

Here, CF (calfactor) and OS (offset) are two parameters (P) of anexample model that is capable of capturing a linear relationship betweena sensor current signal and blood glucose concentration.

In order to estimate parameters for the above example model, bloodglucose concentration may be sampled periodically with the help of a,e.g., finger-stick BG-meter. An accurate estimation of parameters may bechallenging if sensor characteristics change significantly with time.Moreover, infrequent reference blood glucose measurement samples maymake parameter estimation even more difficult. On the other hand,finding appropriate calfactor and offset values may improve sensorperformance, especially during hyperglycemic and hypoglycemic periods.The following description contains three different example techniquesthat may be used to estimate parameters of Equations (16) and (17).Example methods to evaluate a performance of each technique are alsodescribed below. Although three parameter estimation techniques thatutilize at least one probability model are described herein below,claimed subject matter is not so limited, and other parameter estimationtechniques may alternatively be implemented.

FIG. 19 is a block diagram 1900 that illustrates an example estimator1902 and calibration model unit 1904 to determine sensor glucose (SG) inaccordance with an embodiment. As illustrated, block diagram 1900 mayinclude sensor current (isig) and reference blood glucose concentration(BG) input signals to estimator 1902. Estimator 1902 may provideestimated parameters (P) and a likelihood (LH) value. Calibration modelunit 1904 may produce sensor glucose SG from sensor current isig andparameters P.

For certain example embodiments, block diagram 1900 illustrates anexample parameter estimation for a sensor calibration model. Estimator1902 may represent any type of estimation approach whose function is toestimate parameters of Equation (16), including one or more probabilitymodels. Example estimation approaches include, but are not limited to, aKalman filter (KF), a Kalman filter with adaptive process noise matrix(KFQ), an unscented Kalman filter, a Bayesian inference algorithm (B),some combination thereof, and so forth.

Input data to estimator 1902 may comprise sensor current (isig) andreference blood glucose concentration (BG). Output data of estimator1902 may comprise an estimated parameter vector, P (e.g., P=[CF; OS] forEquation (16)), and a likelihood value, LH. A likelihood value LH, ormore generally a quality indicator, may indicate a performance level ofa calibration model. A parameter vector, P, along with sensor current,isig, may be fed to a calibration model unit 1904 in order to calculatesensor glucose concentration (SG). Although one estimator 1902 and onecalibration model unit 1904 are shown in block diagram 1900, more thanone of either or both may be implemented. For example, two estimators,E1 and E2, may be used in parallel, as is described herein below withparticular reference to FIG. 21. Although a likelihood value isdescribed as an example quality indicator to indicate a performanceaccuracy of a calibration model, claimed subject matter is not solimited, and other quality indicators may alternatively be implemented.

FIG. 20 is a flow diagram 2000 of an example method for onlinecalibration of sensor measurements using one or more probability modelsin accordance with an embodiment. As illustrated, flow diagram 2000includes three operations 2002-2006. For certain example embodiments, atoperation 2002, multiple blood glucose reference sample-sensormeasurement pairs may be obtained. At operation 2004, one or moreparameters of a probability model may be estimated based, at least inpart, on the multiple blood glucose reference sample-sensor measurementpairs. Such an estimation of parameter(s) may be performed, for example,by estimator 1902.

At operation 2006, a function for estimating a blood-glucoseconcentration in a patient from sensor measurements may be determinedbased, at least in part, on the estimated one or more parameters. Such adetermination of a function may be performed, for example, bycalibration model unit 1904. Although a certain number of operations arespecifically illustrated in each flow diagram that is described herein,other embodiments may have a different number and/or differentoperations without departing from claimed subject matter.

Three example probability models are described below that may be used toestimate parameters P by an estimator 1902. However, embodiments mayimplement alternative probability model(s) without departing fromclaimed subject matter. Example Linear Kalman Filter (KF): A linearKalman Filter (KF) may have two stages: a prediction stage, in which acurrent stage of a system is predicted given a previous stage; and anupdate stage, in which a current predicted stage of the system isupdated/corrected based on a weighted error generated between a modelprediction and a true measurement. Certain example implementations forprediction stages and update stages may be realized using the followingEquations (18)-(24):

$\begin{matrix}{{Prediction}\text{:}} & \; \\{x_{k}^{-} = {A_{k - 1}x_{k - 1}}} & (18) \\{P_{k}^{-} = {{A_{k - 1}P_{k - 1}^{-}A_{k - 1}^{T}} + Q_{k - 1}}} & (19) \\{{Update}\text{:}} & \; \\{v_{k} = {y_{k} - {H_{K}x_{k}^{-}}}} & (20) \\{S_{k} = {{H_{k}P_{k}^{-}H_{k}^{T}} + R_{k}}} & (21) \\{K_{k} = \frac{P_{k}^{-}H_{k}^{T}}{S_{k}}} & (22) \\{x_{k} = {x_{k}^{-} + {K_{k}v_{k}}}} & (23) \\{P_{k} = {P_{k}^{-} - {K_{k}S_{k}K_{k}^{T}}}} & (24)\end{matrix}$

Here, x _(k) and P _(k) may be a predicted mean and a covariance of thestate, respectively, on a time stage k prior to a measurement. In thiscase, x _(k) =[CF⁻; CFOS⁻]. Variables x_(k) and P_(k) may comprise acorrected mean and a covariance of a state, respectively, on a timestage k after seeing a measurement. Variable v_(k) may be a measurementresidual on time stage k. S_(k) may be a measurement predictioncovariance, and K_(k) may be a filter gain on time stage k. A_(k-1) mayrepresent a transition matrix of a dynamic model for a time stage k−1,and H_(k) may be a measurement model matrix for a time stage k. In thiscase, H_(k)=[isig(k), −1]. The superscript ‘-’ sign indicates that suchvariables correspond to prior to an update at a given time stage.

Q_(k-1) may be a process noise on time stage k−1, and R_(k) may bemeasurement noise on time stage k. Both of these noise matrices may bemaintained constant throughout the length of an operation. Also, y_(k)may be an available measurement on a time stage k. A parameter vector,x_(k) (where x_(k)=[CF; CFOS]), may be updated whenever blood glucosemeasurement (y_(k)=BG(k)) is available.

Example Linear Kalman Filter With Adaptive Process Noise Matrix (Q): Alinear Kalman filter with an adaptive Q-matrix (KFQ) has similaroperating stages as those described above with regard to a Kalman filter(KF). A difference is that a process noise matrix, Q_(k), may be afunction of model performance. If BG is available, Q_(k) may be updatedas shown below in Equation (25)

Q _(k) =K _(k)(y _(k) −H _(k) x _(k) ⁻)² K _(k).  (25)

A larger residual may result in a larger Q-matrix, which may in turnlead to a smaller K_(k) per stage. In other words, if a model were notperforming well due to a large process noise, a resulting Kalman gain(K_(k)) may be less aggressive. Such an adaptive feature may be relevantto a system where sensor related artifacts and/or noises are relativelycommon. Potentially, an example KFQ implementation may reduce thechances of capturing noise in estimated parameters.

Example Linear Kalman Filter With Adaptive Measurement Noise Matrix (R):A linear Kalman filter with an adaptive R-matrix (KFR) has similaroperating stages as those described above with regard to a Kalman filter(KF). A difference is that a measurement noise matrix, R_(k), may be afunction of model performance. If BG is available, R_(k) may be updatedas shown below in Equation (26)

R _(k)=(y _(k) −H _(k) x _(k) ⁻)²  (26)

A larger residual may result in a larger R-matrix, which may in turnlead to a smaller K_(k) per stage. In other words, if a model were notperforming well due to a large measurement noise, a resulting Kalmangain (K_(k)) may be less aggressive. Hence, an implementation of KFR mayreduce the chances of capturing noise in estimated parameters. As notedabove, a linear Kalman filter may also be implemented with regard toboth process noise and measurement noise. For example, a KF may beimplemented in which both an adaptive Q-matrix and an adaptive R-matrixare updated based on a residual.

Example Bayesian Inference Approach (B): A general representation of asensor glucose calibration model in terms of an example Bayesianinference approach can be written as follows:

y=ƒ(X,θ).  (27)

Here, X may be an independent variable (isig), and 8 may be a parametervector (θ=[CF; OS]). A dependent variable (SG) may be represented by y.A Bayesian approach (B) may represent uncertainty about unknownparameter values by probability distributions, and it may proceed as ifparameters were random quantities.

A posterior parameter distribution may be given by Baye's theorem asfollows:

$\begin{matrix}{{{\pi \left( {\theta Y} \right)} = \frac{{\pi (\theta)}{\pi \left( {Y\theta} \right)}}{\int{{\pi (\theta)}{\pi \left( {Y\theta} \right)}{\theta}}}},} & (28)\end{matrix}$

where Y may be a vector of measurements, π(θ) may be a prior parameterdistribution, π(θ|Y) may be a posterior parameter distribution, andπ(Y|θ) may be a likelihood function. The likelihood, for certain exampleimplementations, may be a probability of data Y given parameters θ.Likelihood values may be determined from a probability distribution oferrors between modeled and observed data. However, analyticalintegration of the denominator in Equation (27) has been a source ofsome difficulty in applications of Bayesian inference. Monte Carlointegration using Markov Chain Monte Carlo (MCMC) is one approach toaddressing this difficulty.

A Metropolis-Hastings algorithm: A Metropolis-Hastings (MH) algorithm isan example MCMC technique for generating samples from a posteriordistribution π(θ|Y). An MH algorithm may start with a vector value θ₀.For example implementations, a sequence of N parameter vectors θ_(i),i=1, . . . , N, may be generated as follows:

-   -   1. Generate a candidate parameter vector θ* from a proposal        distribution for instance a normal distribution with mean equal        to θ_(i-1).    -   2. Calculate T in accordance with Equation (29) below:

$\begin{matrix}{T = \frac{{\pi \left( {Y\theta^{*}} \right)}{\pi \left( \theta^{*} \right)}{q\left( {\theta_{i - 1}\theta^{*}} \right)}}{{\pi \left( {Y\theta_{i - 1}} \right)}{\pi \left( \theta_{i - 1} \right)}{q\left( {\theta^{*}\theta^{i - 1}} \right)}}} & (29)\end{matrix}$

-   -   where π(Y|θ*) and π(Y|θ_(i-1)) may be maximum likelihood values        of parameter vectors θ* and θ_(i-1), respectively, and π(θ*) and        π(θ_(i-1)) may be prior densities of θ* and θ_(i-1),        respectively.    -   3. If min(1,T)>u, where u is drawn from a uniform distribution        on the interval (0,1), then θ_(i)=θ*, else θ_(i)=θ_(i-1).

After an initial phase of say M iterations, a chain thus constructed maylikely converge to a chain with elements randomly drawn from a posteriorparameter distribution π(θ|Y). A first M iterations may be discarded. Asample of parameter vectors θ_(i), i=M+1, . . . , N, may be used tocalculate a posterior means as follows using Equation (30):

$\begin{matrix}{\overset{\_}{\theta} = {\frac{1}{N - M}{\sum\limits_{i = {M + 1}}^{N}\theta_{i}}}} & (30)\end{matrix}$

Vector θ may be considered as an estimate of model parameters. A sampleof parameter vectors may also be used for calculating posteriorvariances, correlations between parameters, and distribution of modelprediction.

Example calculation of maximum likelihood function: The followingexample function of Equation (31) may be used to calculate a maximumlikelihood:

$\begin{matrix}{{\pi \left( {Y\theta} \right)} = {\prod\limits_{j = 1}^{K}{\left( {2{\pi\sigma}_{j}^{2}} \right)^{{- 1}/2}\exp \left\{ \frac{- \left\lbrack {y_{j} - {f\left( {x_{j};\theta} \right)}} \right\rbrack^{2}}{2\sigma_{j}^{2}} \right\}}}} & (31)\end{matrix}$

where y_(j) may be a jth y value in a dataset Y, x_(j) may be a vectorof explanatory (e.g., independent) variables associated with y_(j),f(x_(j),θ) may be a model prediction of y_(j), σ_(j) may be a standarddeviation associated with the jth value in a data set, and K may be atotal number of data in a data set.

Generally, parameter estimation by a Bayesian approach is relativelymore robust when both process and measurement noises are present.However, a drawback of such a method is that Bayesian approaches can berelatively computationally intensive.

An example technique for evaluating estimator performance is described.For certain example embodiments, a performance of each estimator (e.g.,estimator 1902) may be evaluated based on a bias (e.g., error) betweenmodel estimates and “true” values. Many different functions may be usedto evaluate estimator performance. If model errors are assumed to beindependent and normally distributed, the following likelihood function,by way of example but not limitation, may be used:

$\begin{matrix}{{LH} = {\frac{1}{\sigma_{j}\sqrt{2\pi}}\exp \left\{ \frac{- \left\lbrack {y_{j} - {\overset{\_}{y}}_{j}} \right\rbrack^{2}}{2\sigma_{j}^{2}} \right\}}} & (32)\end{matrix}$

Here, y_(j) may be a “true” value (e.g., data) at a jth stage, and y_(j) may be a model prediction at a jth stage (where, y_(j)=CF×isig_(j)−CFOS). σ_(j) may represent a standard deviation of amodel estimate at a jth stage due to variation in CF and CFOS parametervalues.

Generally, a larger model error may result in a drop in an LH value.During a calibration phase, after detecting a measurement if anestimated LH value is less than a prior LH value by a certainpredetermined margin, an extra confirmation finger-stick measurement maybe requested. Although a particular example (e.g., LH-based) functionhas been used to evaluate performance of estimator(s), claimed subjectmatter is not so limited, and other (e.g., error-characterizing)evaluation functions may alternatively be implemented.

FIG. 21 is a block diagram 2100 that illustrates multiple exampleestimators 1902 and calibration model units 1904 to determine acomposite sensor glucose (SG_(COMP)) in accordance with an embodiment.As illustrated, block diagram 2100 may include two estimators 1902-E1and 1902-E2, two calibration model units 1904-C1 and 1904-C2, aweighting unit 2102, and two mixers 2104-M1 and 2104-M2. It may alsoinclude a combining unit 2106.

For certain example embodiments, two estimators, E1 and E2, may be usedin parallel. Based on performances of an E1-estimator (LH_(E1)) and anE2-estimator (LH_(E2)), respective weights at weighting unit 2102 may beassigned (x_(E1) and 1−x_(E1)) to respective sensor glucoseconcentrations (SG_(E1) and SG_(E2)). These respective sensor glucoseconcentrations (SG_(E1) and SG_(E2)) may be provided by respectivecalibration models, C1 and C2. Respective weights and sensor glucoseconcentrations (as indicated by E1 and/or E2 in block diagram 2100) maybe provided to mixers M1 and M2. Output signals from mixers M1 and M2may be used to calculate a composite sensor glucose concentration(SG_(comp)) with combining unit 2106.

As shown in FIG. 21, more than one estimator may be used in parallel tocalculate a sensor glucose concentration (SG). For example, twoestimators E1 and E2 may be implemented. In such cases, a Kalman filter(KF) and a Bayesian inference estimator (B), for instance, may be usedto estimate parameters of an, e.g., linear model, relatively oreffectively simultaneously. A contribution of each estimator may becomputed by comparing their performances in terms of a, e.g., likelihoodfunction at a weighting unit 2102.

Such a comparison may be achieved, for example, by using the followingEquation (33):

$\begin{matrix}{x_{E\; 1} = \frac{{LH}_{E\; 1}}{{LH}_{E\; 1} + {LH}_{E\; 2}}} & (33)\end{matrix}$

Here, LH_(E1) and LH_(E2) may be likelihood values of E1- andE2-estimators (e.g., KF- and B-estimators), respectively. x_(E1) may bea fractional weight of an E1-estimator.

Conversely, 1−x_(E1) may be a fractional weight of an E2-estimator. Acomposite sensor glucose (SG_(comp)) value from both of these two ormore estimators may be calculated as follows using example Equation (34)with mixers 2104-M1 and 2104-M2 plus combiner 2106:

SG_(comp)=SG_(E1)×x_(E1)SG_(E2)×(1−x_(E1))  (34)

Here, SG_(E1) and SG_(E2) may be sensor glucose concentrationscalculated by E1- and E2-estimators, respectively. Although FIG. 21includes two estimators, claimed subject matter is not so limited, andresults from three or more estimators may also be combined into acomposite sensor glucose value. Also, although an example abovedescribes using one KF estimator and one B estimator, claimed subjectmatter is not so limited. Alternative estimation approaches may beimplemented instead. Furthermore, other combinations (including multipleestimators based on a same approach) may also be implemented.

Unless specifically stated otherwise, as is apparent from the precedingdiscussion, it is to be appreciated that throughout this specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “correlating”, “determining”, “estimating”, “selecting”,“identifying”, “obtaining”, “representing”, “receiving”, “transmitting”,“storing”, “analyzing”, “associating”, “measuring”, “detecting”,“controlling”, “delaying”, “initiating”, “setting”, “providing”, and soforth may refer to actions, processes, etc. that may be partially orfully performed by a specific apparatus, such as a special purposecomputer, special purpose computing apparatus, a similar special purposeelectronic computing device, and so forth, just to name a few examples.In the context of this specification, therefore, a special purposecomputer or a similar special purpose electronic computing device may becapable of manipulating or transforming signals, which are typicallyrepresented as physical electronic and/or magnetic quantities withinmemories, registers, or other information storage devices; transmissiondevices; display devices of a special purpose computer; or similarspecial purpose electronic computing device; and so forth, just to namea few examples. In particular example embodiments, such a specialpurpose computer or similar may comprise one or more processorsprogrammed with instructions to perform one or more specific functions.Accordingly, a special purpose computer may refer to a system or adevice that includes an ability to process or store data in the form ofsignals. Further, unless specifically stated otherwise, a process ormethod as described herein, with reference to flow diagrams orotherwise, may also be executed or controlled, in whole or in part, by aspecial purpose computer.

It should be understood that aspects described above are examples onlyand that embodiments may differ there from without departing fromclaimed subject matter. Also, it should be noted that although aspectsof the above systems, methods, apparatuses, devices, processes, etc.have been described in particular orders and in particular arrangements,such specific orders and arrangements are merely examples and claimedsubject matter is not limited to the orders and arrangements asdescribed. It should additionally be noted that systems, devices,methods, apparatuses, processes, etc. described herein may be capable ofbeing performed by one or more computing platforms.

In addition, instructions that are adapted to realize methods,processes, etc. that are described herein may be capable of being storedon a storage medium as one or more machine readable instructions. Ifexecuted, machine readable instructions may enable a computing platformto perform one or more actions. “Storage medium” as referred to hereinmay relate to media capable of storing information or instructions whichmay be operated on, or executed, by one or more machines (e.g., thatinclude at least one processor). For example, a storage medium maycomprise one or more storage articles and/or devices for storingmachine-readable instructions or information. Such storage articlesand/or devices may comprise any one of several media types including,for example, magnetic, optical, semiconductor, a combination thereof,etc. storage media. By way of further example, one or more computingplatforms may be adapted to perform one or more processes, methods, etc.in accordance with claimed subject matter, such as methods, processes,etc. that are described herein. However, these are merely examplesrelating to a storage medium and a computing platform and claimedsubject matter is not limited in these respects.

Although there have been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from central concepts that are described herein. Therefore, itis intended that claimed subject matter not be limited to particularexamples disclosed, but that such claimed subject matter may alsoinclude all aspects falling within the scope of appended claims, andequivalents thereof.

1. A method comprising: correlating blood glucose reference samples withsensor measurements to provide at least one output signal responsive toa delay associated with said sensor measurements; and determining afunction for estimating a blood-glucose concentration in a patient fromsensor measurements based, at least in part, on said at least one outputsignal.
 2. The method of claim 1, wherein said correlating comprises:applying said blood glucose reference samples and said sensormeasurements to a matched filter at a plurality of time shift delays toascertain said delay.
 3. The method of claim 1, wherein said correlatingcomprises: correlating said sensor measurements with said blood glucosereference samples at a plurality of different time delays to ascertainsaid delay.
 4. The method of claim 1, wherein said determiningcomprises: applying said blood glucose reference samples and said sensormeasurements to a Wiener filter in conjunction with said delay todetermine multiple filter coefficients.
 5. The method of claim 1,wherein said determining further comprises: determining a function forestimating a blood-glucose concentration in the patient from sensormeasurements based, at least in part, on a noise signal that isassociated with said sensor measurements.
 6. The method of claim 1,wherein said sensor measurements comprise current sensor measurementstaken from interstitial fluid of the patient.
 7. The method of claim 1,wherein said determining further comprises: determining a function forestimating a blood-glucose concentration in the patient, said functionto account for said delay; said delay representing, at least partially,an approximated delay associated with blood glucose diffusion betweenone or more blood vessels and interstitial fluid of the patient.
 8. Themethod of claim 1, wherein said determining further comprises:determining a slope and an offset for said function for estimating ablood-glucose concentration in a patient.
 9. The method of claim 8,wherein said determining further comprises: determining said slope andsaid offset for said function using a Bayesian technique in which aparameter vector includes a calfactor variable and an offset variableand in which an independent variable includes a current signalcorresponding to said sensor measurements.
 10. The method of claim 8,wherein said determining further comprises: determining said slope andsaid offset for said function using a linear Kalman filter technique inwhich a parameter vector includes a calfactor variable and an offsetvariable.
 11. The method of claim 1, further comprising: taking saidsensor measurements that are to be correlated using one or moresubcutaneous current sensors; and infusing insulin into the patientbased on said function for estimating a blood-glucose concentration inthe patient.
 12. A apparatus comprising: a filter unit to receive one ormore signals based on blood-glucose sensor measurements, said filterunit comprising one or more processors to: correlate blood glucosereference samples with sensor measurements to provide at least oneoutput signal responsive to a delay associated with said sensormeasurements; and determine a function for estimating a blood-glucoseconcentration in a patient from sensor measurements based, at least inpart, on said at least one output signal.
 13. The apparatus of claim 12,wherein said filter unit is capable of correlating said blood glucosereference samples with said sensor measurements by: applying said bloodglucose reference samples and said sensor measurements to a matchedfilter at a plurality of time shift delays to ascertain said delay. 14.The apparatus of claim 12, wherein said filter unit is capable ofcorrelating said blood glucose reference samples with said sensormeasurements by: correlating said sensor measurements with said bloodglucose reference samples at a plurality of different time delays toascertain said delay.
 15. The apparatus of claim 12, wherein said filterunit is capable of determining said function for estimating saidblood-glucose concentration in the patient by: applying said bloodglucose reference samples and said sensor measurements to a Wienerfilter in conjunction with said delay to determine multiple filtercoefficients.
 16. The apparatus of claim 12, wherein said filter unit iscapable of determining said function for estimating said blood-glucoseconcentration in the patient by: determining a function for estimating ablood-glucose concentration in the patient from sensor measurementsbased, at least in part, on a noise signal that is associated with saidsensor measurements.
 17. The apparatus of claim 12, wherein said sensormeasurements comprise current sensor measurements taken frominterstitial fluid of the patient.
 18. The apparatus of claim 12,wherein said filter unit is capable of determining said function forestimating said blood-glucose concentration in the patient by:determining a function for estimating a blood-glucose concentration inthe patient, said function to account for said delay; said delayrepresenting, at least partially, an approximated delay associated withblood glucose diffusion between one or more blood vessels andinterstitial fluid of the patient.
 19. The apparatus of claim 12,wherein said filter unit is capable of determining said function forestimating said blood-glucose concentration in the patient by:determining a slope and an offset for said function for estimating ablood-glucose concentration in a patient.
 20. The apparatus of claim 19,wherein said filter unit is capable of determining said function forestimating said blood-glucose concentration in the patient by:determining said slope and said offset for said function using aBayesian technique in which a parameter vector includes a calfactorvariable and an offset variable and in which an independent variableincludes a current signal corresponding to said sensor measurements. 21.The apparatus of claim 19, wherein said filter unit is capable ofdetermining said function for estimating said blood-glucoseconcentration in the patient by: determining said slope and said offsetfor said function using a linear Kalman filter technique in which aparameter vector includes a calfactor variable and an offset variable.22. The apparatus of claim 12, further comprising: one or moreblood-glucose subcutaneous current sensors adapted to be coupled to thepatient to obtain blood-glucose sensor measurements and adapted toprovide said one or more signals based on said blood-glucose sensormeasurements; wherein said filter unit is capable of obtaining saidblood-glucose sensor measurements via said one or more blood-glucosesubcutaneous current sensors using said one or more signals; and atleast one insulin delivery system adapted to infuse insulin into thepatient based on said function for estimating a blood-glucoseconcentration in the patient.
 23. A system comprising: means forcorrelating blood glucose reference samples with sensor measurements toprovide at least one output signal responsive to a delay associated withsaid sensor measurements; and means for determining a function forestimating a blood-glucose concentration in a patient from sensormeasurements based, at least in part, on said at least one outputsignal.
 24. An article comprising: at least one storage medium havingstored thereon instructions executable by one or more processors to:correlate blood glucose reference samples with sensor measurements toprovide at least one output signal responsive to a delay associated withsaid sensor measurements; and determine a function for estimating ablood-glucose concentration in a patient from sensor measurements based,at least in part, on said at least one output signal.
 25. A methodcomprising: obtaining a plurality of blood glucose referencesample-sensor measurement pairs; estimating one or more parameters of aprobability model based, at least in part, on said blood glucosereference sample-sensor measurement pairs; and determining a functionfor estimating a blood-glucose concentration in a patient from sensormeasurements based, at least in part, on said estimated one or moreparameters.
 26. The method of claim 25, wherein said obtainingcomprises: taking sensor measurements for said plurality of bloodglucose reference sample-sensor measurement pairs using at least onesubcutaneous current sensor.
 27. The method of claim 25, wherein saidfunction is defined at least partly by a slope and an offset.
 28. Themethod of claim 25, wherein said one or more parameters comprise acalfactor variable and an offset variable.
 29. The method of claim 28,wherein a relationship between current sensor measurements correspondingto said blood glucose reference sample-sensor measurement pairs andsensor glucose concentration for the patient may be represented by alinear model, said linear model associated with a slope and an offset;and wherein said slope and said offset of said linear model aredeterminable from said calfactor variable and/or said offset variable.30. The method of claim 25, wherein said estimating further comprises:estimating said one or more parameters using a linear Kalman filterestimator in which process noise and measurement noise are modeled asbeing constant.
 31. The method of claim 25, wherein said estimatingfurther comprises: estimating said one or more parameters using a linearKalman filter estimator in which process noise is adapted as a functionof model performance.
 32. The method of claim 25, wherein saidestimating further comprises: estimating said one or more parametersusing a linear Kalman filter estimator in which measurement noise isadapted as a function of model performance.
 33. The method of claim 25,wherein said estimating further comprises: estimating said one or moreparameters using a Bayesian estimator in which sensor measurementscorresponding to said blood glucose reference sample-sensor measurementpairs comprise an independent variable and blood glucose referencesamples corresponding to said blood glucose reference sample-sensormeasurement pairs comprise a measured variable.
 34. The method of claim25, further comprising: estimating a composite sensor glucoseconcentration value for the patient using a plurality of functions andat least one weighting factor that is derived from one or more qualityindicators for a plurality of probability models.
 35. The method ofclaim 25, wherein said determining further comprises determining aplurality of functions for estimating a blood-glucose concentration inthe patient from sensor measurements; and wherein the method furthercomprises: estimating a plurality of sensor glucose concentration valuesfor the patient using said plurality of functions; said plurality offunctions associated with a plurality of probability models, whichinclude said probability model; and determining a composite sensorglucose concentration value for the patient based, at least partly, onsaid plurality of sensor glucose concentration values.
 36. The method ofclaim 35, wherein said determining a composite sensor glucoseconcentration value for the patient comprises: weighting said pluralityof sensor glucose concentration values based, at least in part, on aplurality of quality indicators indicative of an accuracy of saidplurality of probability models.
 37. The method of claim 36, whereinsaid plurality of quality indicators comprise a plurality of likelihoodvalues; and wherein said method further comprises: calculating saidplurality of likelihood values based, at least partly, on errordifferences between sensor glucose concentration values estimated fromsaid plurality of probability models and reference blood glucose valuesfrom blood glucose reference samples.
 38. An apparatus comprising: acalibration unit to receive one or more signals based on blood-glucosesensor measurements, said calibration unit comprising one or moreprocessors to: obtain a plurality of blood glucose referencesample-sensor measurement pairs; estimate one or more parameters of aprobability model based, at least in part, on said blood glucosereference sample-sensor measurement pairs; and determine a function forestimating a blood-glucose concentration in a patient from sensormeasurements based, at least in part, on said estimated one or moreparameters.
 39. The apparatus of claim 38, wherein said calibration unitis capable of obtaining said plurality of blood glucose referencesample-sensor measurement pairs by: taking sensor measurements for saidplurality of blood glucose reference sample-sensor measurement pairsusing at least one subcutaneous current sensor.
 40. The apparatus ofclaim 38, wherein said function is defined at least partly by a slopeand an offset.
 41. The apparatus of claim 38, wherein said one or moreparameters comprise a calfactor variable and an offset variable.
 42. Theapparatus of claim 41, wherein a relationship between current sensormeasurements corresponding to said blood glucose reference sample-sensormeasurement pairs and sensor glucose concentration for the patient maybe represented by a linear model, said linear model associated with aslope and an offset; and wherein said slope and said offset of saidlinear model are determinable from said calfactor variable and/or saidoffset variable.
 43. The apparatus of claim 38, wherein said calibrationunit is capable of estimating said one or more parameters of aprobability model by: estimating said one or more parameters using alinear Kalman filter estimator in which process noise and measurementnoise are modeled as being constant.
 44. The apparatus of claim 38,wherein said calibration unit is capable of estimating said one or moreparameters of a probability model by: estimating said one or moreparameters using a linear Kalman filter estimator in which process noiseis adapted as a function of model performance.
 45. The apparatus ofclaim 38, wherein said calibration unit is capable of estimating saidone or more parameters of a probability model by: estimating said one ormore parameters using a linear Kalman filter estimator in whichmeasurement noise is adapted as a function of model performance.
 46. Theapparatus of claim 38, wherein said calibration unit is capable ofestimating said one or more parameters of a probability model by:estimating said one or more parameters using a Bayesian estimator inwhich sensor measurements corresponding to said blood glucose referencesample-sensor measurement pairs comprise an independent variable andblood glucose reference samples corresponding to said blood glucosereference sample-sensor measurement pairs comprise a measured variable.47. The apparatus of claim 38, wherein said one or more processors ofsaid calibration unit are further to: estimate a composite sensorglucose concentration value for the patient using a plurality offunctions and at least one weighting factor that is derived from one ormore quality indicators for a plurality of probability models.
 48. Theapparatus of claim 38, wherein said calibration unit is capable ofdetermining said function for estimating a blood-glucose concentrationin the patient by determining a plurality of functions for estimating ablood-glucose concentration in the patient from sensor measurements; andwherein said one or more processors of said calibration unit are furtherto: estimate a plurality of sensor glucose concentration values for thepatient using said plurality of functions; said plurality of functionsassociated with a plurality of probability models, which include saidprobability model; and determine a composite sensor glucoseconcentration value for the patient based, at least partly, on saidplurality of sensor glucose concentration values.
 49. The apparatus ofclaim 48, wherein said calibration unit is capable of determining saidcomposite sensor glucose concentration value for the patient by:weighting said plurality of sensor glucose concentration values based,at least in part, on a plurality of quality indicators indicative of anaccuracy of said plurality of probability models.
 50. The apparatus ofclaim 49, wherein said plurality of quality indicators comprise aplurality of likelihood values; and wherein said one or more processorsof said calibration unit are further to: calculate said plurality oflikelihood values based, at least partly, on error differences betweensensor glucose concentration values estimated from said plurality ofprobability models and reference blood glucose values from blood glucosereference samples.
 51. The apparatus of claim 38, further comprising:one or more blood-glucose sensors adapted to be coupled to the patientto obtain blood-glucose sensor measurements and adapted to provide saidone or more signals based on said blood-glucose sensor measurements,wherein said calibration unit is capable of obtaining said plurality ofblood glucose reference sample-sensor measurement pairs via said one ormore blood-glucose sensors using said one or more signals.
 52. A systemcomprising: means for obtaining a plurality of blood glucose referencesample-sensor measurement pairs; means for estimating one or moreparameters of a probability model based, at least in part, on said bloodglucose reference sample-sensor measurement pairs; and means fordetermining a function for estimating a blood-glucose concentration in apatient from sensor measurements based, at least in part, on saidestimated one or more parameters.
 53. An article comprising: at leastone storage medium having stored thereon instructions executable by oneor more processors to: obtain a plurality of blood glucose referencesample-sensor measurement pairs; estimate one or more parameters of aprobability model based, at least in part, on said blood glucosereference sample-sensor measurement pairs; and determine a function forestimating a blood-glucose concentration in a patient from sensormeasurements based, at least in part, on said estimated one or moreparameters.